WO2017031440A1 - Conseiller de thérapie pour la gestion du diabète - Google Patents

Conseiller de thérapie pour la gestion du diabète Download PDF

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Publication number
WO2017031440A1
WO2017031440A1 PCT/US2016/047806 US2016047806W WO2017031440A1 WO 2017031440 A1 WO2017031440 A1 WO 2017031440A1 US 2016047806 W US2016047806 W US 2016047806W WO 2017031440 A1 WO2017031440 A1 WO 2017031440A1
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WIPO (PCT)
Prior art keywords
patient
insulin
dose
training
program
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Application number
PCT/US2016/047806
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English (en)
Inventor
Robert C. Booth
Harry Hebblewhite
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Aseko, Inc.
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Publication date
Application filed by Aseko, Inc. filed Critical Aseko, Inc.
Priority to JP2018509740A priority Critical patent/JP6858751B2/ja
Priority to AU2016308953A priority patent/AU2016308953B2/en
Priority to EP16837919.6A priority patent/EP3337402A4/fr
Priority to CA2993275A priority patent/CA2993275C/fr
Publication of WO2017031440A1 publication Critical patent/WO2017031440A1/fr
Priority to IL256999A priority patent/IL256999B/en

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient ; user input means
    • A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G16H20/17—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/60—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H70/00—ICT specially adapted for the handling or processing of medical references
    • G16H70/20—ICT specially adapted for the handling or processing of medical references relating to practices or guidelines

Definitions

  • This disclosure relates to a diabetes management therapy advisor for identifying and optimizing personalized therapies for the treatment of Diabetes Mellitus.
  • Diabetics often have higher rates of cardiovascular, renal, gastrointestinal, neurological, thyroid diseases, and ophthalmological complications compared to people without diabetes.
  • Patients with diabetes often may receive a wide array of medications including injectable long-acting and rapid-acting insulins, inhaled insulin, oral medications, and other injectable anti-diabetic medications.
  • some medications are contraindicated for pregnancy or patients with severe kidney disease.
  • other medications which are appropriate for the treatment of Type 2 diabetes are contraindicated for Type 1 diabetes.
  • One aspect of the disclosure provides a method for determining a treatment dose for a treated patient.
  • the method includes obtaining, at data processing hardware, training data for a plurality of patients of a patient population from memory hardware in communication with the data processing hardware.
  • the training data includes training blood glucose history data and training patient-state information for each patient of the patient population.
  • the training blood glucose history data includes treatment doses of insulin administered by the patients of the patient population and one or more outcome attributes associated with each treatment dose of insulin administered by the patients of the patient population.
  • the method includes identifying, using the data processing hardware, one or more optimum treatment doses of insulin from the treatment doses of insulin yielding favorable outcome attributes.
  • the method also includes receiving, at the data processing hardware, patient-state information for the treated patient.
  • the method also includes determining, using the data processing hardware, a next recommended treatment dose of insulin for the treated patient based on one or more of the identified optimum treatment doses associated with the patients of the patient population having training patient-state information similar to the patient-state information for the treated patient.
  • the method further includes transmitting the next recommended treatment dose to a portable device associated with the treated patient, the portable device displaying the next recommended insulin dose.
  • Implementations of the disclosure may include one or more of the following optional features.
  • obtaining the training data includes obtaining the training data automatically at an end of a re-occurring configurable time interval.
  • Obtaining the training data may include obtaining the training data immediately in response to a user input selecting an immediate start button displayed upon a display in communication with the data processing hardware.
  • Obtaining the training data may also include obtaining the training data on a selected date.
  • determining the next recommended treatment dose of insulin for the treated patient includes determining the treated patient requires insulin based on the patient-state information for the treated patient and receiving meal boluses of insulin previously administered by the treated patient during a scheduled time-interval. Determining a next recommended meal bolus during the scheduled time interval for the treated patient may be based on at least one of the identified optimum treatment doses associated with the scheduled time interval and the received meal boluses of insulin previously administered by the treated patient during the scheduled time interval.
  • the scheduled time interval may include a pre-breakfast time interval, a pre-lunch time interval, a pre-dinner time interval, a bedtime time interval, or a midsleep time interval.
  • One or more outcome attributes of the training blood glucose history data may include a blood glucose percent error based on a function of a next scheduled blood glucose measurement and a blood glucose target range.
  • the next scheduled blood glucose measurement may correspond to a blood glucose measurement occurring after administration of a corresponding treatment dose of insulin.
  • Determining the next recommended treatment dose of insulin for the treated patient may include determining the treated patient requires insulin based on the patient-state information for the treated patient and receiving basal doses of insulin previously administered by the treated patient. Determining a next recommended basal dose for the treated patient may be based on at least one of the identified optimum treatment doses and the received basal doses of insulin previously administered by the treated patient.
  • the method includes transmitting the next recommended treatment dose of insulin to an administration device in communication with the data processing hardware.
  • the administration device may include a doser and an
  • the administration computing device in communication with the doser.
  • the administration computing device may cause the doser to administer insulin specified by the next recommended treatment dose of insulin.
  • Determining the next recommended treatment dose for the treatment patient may include determining anti-diabetic medications are usable for treating the treated patient based on the patient-state information for the treated patient, receiving a glycated hemoglobin measurement of the patient, and determining an anti-diabetes medication regimen for the treated patient based on the glycated
  • hemoglobin measurement and the training data.
  • the treatment doses of the training data may correspond to anti-diabetes medication dose-combinations administered by patients of the patient population.
  • the outcome attributes of the training data may correspond to a glycated hemoglobin measurement associated with each anti-diabetes medication regimen.
  • the patient-state information may include a plurality of patient-state attributes associated with the patient.
  • the patient-state attributes may include one or more of an age, a gender, a medical history, a body mass index, a medical history, risk factors, and/or financial attributes.
  • the system includes a dosing controller including data processing hardware and memory hardware in communication with the data processing hardware.
  • the dosing controller obtains training data for a plurality of patients of a patient population from the memory hardware.
  • the training data includes training blood glucose history data and training patient-state information for each patient of the patient population.
  • the training blood glucose history data includes treatment doses of insulin administered by the patients of the patient population and one or more outcome attributes associated with each treatment dose of insulin administered by the patients of the patient population.
  • the system includes identifying one or more optimum treatment doses of insulin from the treatment doses of insulin yielding favorable outcome attributes and receiving patient-state information for the treated patient.
  • the system also includes determining a next recommended treatment dose of insulin for the treated patient based on one or more of the identified optimum treatment doses associated with the patients of the patient population having training patient-state information similar to the patient-state information for the treated patient.
  • the system further includes transmitting the next recommended treatment dose to a portable device associated with the treated patient, the portable device displaying the next recommended insulin dose.
  • obtaining the training data includes obtaining the training data automatically at an end of a re-occurring configurable time interval.
  • Obtaining the training data may include obtaining the training data immediately in response to a user input selecting an immediate start button displayed upon a display in communication with the data processing hardware.
  • Obtaining the training data may further include obtaining the training data on a selected date.
  • determining the next recommended treatment dose of insulin for the treated patient comprises determining the treated patient requires insulin based on the patient-state information for the treated patient and receiving meal boluses of insulin previously administered by the treated patient during a scheduled time-interval. Determining a next recommended meal bolus during the scheduled time interval for the treated patient may be based on at least one of the identified optimum treatment doses associated with the scheduled time interval and the received meal boluses of insulin previously administered by the treated patient during the scheduled time interval.
  • the scheduled time interval may include a pre-breakfast time interval, a pre-lunch time interval, a pre-dinner time interval, a bedtime time interval, or a midsleep time interval.
  • the one or more outcome attributes of the training blood glucose history data may include a blood glucose percent error based on a function of a next scheduled blood glucose measurement and a blood glucose target range.
  • the next scheduled blood glucose measurement may correspond to a blood glucose measurement occurring after administration of a corresponding treatment dose of insulin.
  • Determining the next recommended treatment dose of insulin for the treated patient may include determining the treated patient requires insulin based on the patient- state information for the treated patient and receiving basal doses of insulin previously administered by the treated patient. Determining a next recommended basal dose for the treated patient may be based on at least one of the identified optimum treatment doses and the received basal doses of insulin previously administered by the treated patient.
  • the dosing controller may transmit the next recommended treatment dose of insulin to an administration device in communication with the dosing controller.
  • the administration device may include a doser and an administration device in communication with the doser.
  • the administration computing device may cause the doser to administer insulin specified by the next recommended treatment dose of insulin.
  • determining the next recommended treatment dose for the treated patient includes determining anti-diabetic medications are usable for treating the treated patient based on the patient-state information for the treated patient, receiving a glycated hemoglobin measurement of the patient, and determining an anti- diabetes medication regimen for the treated patient based on the glycated hemoglobin measurement and the training data.
  • the treatment doses of the training data may correspond to anti-diabetes medication dose-combinations administered by patients of the patient population.
  • the outcome attributes of the training data may correspond to a glycated hemoglobin measurement associated with each anti-diabetes medication regimen.
  • the patient-state information may include a plurality of patient-state attributes associated with the patient.
  • the patient-state attributes may include one or more of an age, a gender, a medical history, a body mass index, a medical history, risk factors, and/or financial attributes.
  • FIG. 1 A is a schematic view of an exemplary system for recommending treatment doses for a patient.
  • FIG. IB is a schematic view of an exemplary system for recommending treatment doses for a patient.
  • FIG. 1C is a schematic view of an exemplary administration device in communication with a dosing controller.
  • FIG. 2A is a schematic view of an exemplary process for starting a patient treatment program for a patient.
  • FIGS. 2B and 2C are schematic views of an exemplary display for inputting patient information.
  • FIG. 2D is a schematic view of an exemplary display for selecting a start mode of a training program.
  • FIG. 3 A is a schematic view of an exemplary patient training program for adjusting doses of insulin.
  • FIG. 3B is a schematic view of an exemplary patient treatment program using training data for determining an optimum dose of insulin.
  • FIG. 3C is a schematic view of an exemplary anti-diabetes program for prescribing Anti-Diabetes Medication dose-combinations to a patient.
  • FIG. 4A is a schematic view of an exemplary subcutaneous meal bolus adjustment program.
  • FIG. 4B is a schematic view of an exemplary subcutaneous basal adjustment program.
  • FIG. 4C is a schematic view of an anti-diabetes medication adjustment program.
  • FIG. 5 is an exemplary arrangement of operations for determining a treatment dose for a treated patient.
  • Diabetes requires early, continuous, effective, and regular treatment to significantly delay, and in some instances eliminate, the progression of the disease.
  • managing diabetes can be an extremely complex process. Patients with diabetes are often prescribed a large number of different medications for dyslipidemia, hypertension, and control of blood glucose. Diuretic medications often used to treat heart failure, blood pressure, and other kidney disorders, may have an unintended side effect that contributes to hypoglycemia.
  • Healthcare professions must weigh effects and potential pharmacodynamics and pharmacokinetic interactions of concomitant medications.
  • a clinical decision support system 100 analyzes inputted patient condition parameters for a patient 10 and calculates a personalized dose of insulin to bring and maintain the patient's blood glucose level into a target range BG TR .
  • the patient 10 may refer to an outpatient that may be located at some remote location, such as the patient's 10 residence or place of employment, or to an inpatient located at a clinic 42 or hospital.
  • the system 100 monitors the glucose levels of a patient 10 and calculates a recommended
  • the system 100 monitors glycated hemoglobin (hereinafter 'Ale') levels of a patient 10 and calculates a recommended dose of one or more Anti-Diabetes Medications (ADMs) to significantly delay, and in some instances eliminate, the progression of diabetes in the patient 10.
  • ADMs refer to non-insulin medications that may be administered to the patient.
  • a qualified and trained healthcare professional 40 may use the system 100 along with clinical reasoning to determine the proper dosing (e.g., insulin dosing, ADM dosing, or a combination of the two) administered to a patient 10.
  • the system 100 is a glycemic management tool for evaluation of a patient's current and cumulative blood glucose value BG, or current and cumulative Ale level, while taking into consideration the patient's information such as age, weight, and height.
  • the system 100 may also consider other information such as carbohydrate content of meals and/or insulin doses being administered to the patient 10, e.g., long-acting insulin doses for basal insulin and rapid-acting insulin doses for meal boluses and correction boluses.
  • the system 100 recommends a subcutaneous basal and bolus insulin dosing recommendation or prescribed dose to adjust and maintain the blood glucose level towards a configurable (based on the patient's information) physician's determined blood glucose target range BG TR .
  • the system 100 also considers a patient's insulin sensitivity or improved glycemic management and outcomes.
  • the system 100 recommends an ADM dosing recommendation treatment to adjust and maintain the Ale level of the patient 10 towards a configurable (based on the patient's information) physician's determined Ale target range AI C TR .
  • the system 100 may take into account pertinent patient information such as patient-state information and blood glucose BG history data associated with the patient 10.
  • the system 100 may include a domain knowledge base including information about diabetes (that may be stored in non-transitory memory 24, 114, 144), including pertinent information about Type 1 diabetes mellitus (hereinafter 'DM1 ') and Type 2 diabetes mellitus (hereinafter 'DM2').
  • the domain knowledge base may also include information associated with effects and potential pharmacodynamics and pharmacokinetic
  • the system 100 may select a personalized diabetes treatment therapy for glycemic control of the patient 10.
  • the system 100 may also store (in non-transitory memory 24, 114, 144) training blood glucose BG data and patient-state information for a patient population, and use the training BG data for patients of the patient population that have similar patient-state attributes as the patient 10 to adjust a subcutaneous basal and bolus insulin dosing recommendation (or an ADM dosing recommendation) associated with the patient 10.
  • the system 100 provides a reporting platform for reporting the recommendations, adjustments, or prescribed dose(s) to the user 40 and the patient 10.
  • the system 100 provides faster, more reliable, and more efficient insulin administration than a human monitoring the insulin administration.
  • the system 100 reduces human oversight in prescribing medications that may contribute to unintended side effects of hypoglycemia or hyperglycemia due to the system's capability of accessing the information of the domain knowledge base.
  • the system 100 reduces the probability of human error and insures consistent treatment, due to the system's capability of storing and tracking the patient's blood glucose levels BG, which may be used for statistical studies.
  • the system 100 provides a meal-by-meal adjustment of Meal Boluses without carbohydrate counting, by providing a dedicated subprogram that adjusts meal boluses based on the immediately preceding meal bolus and the BG that followed it.
  • the system 100 provides a meal -by-meal adjustment of Meal Boluses with carbohydrate counting by providing a dedicated subprogram that adjusts meal boluses based a
  • CIR Carbohydrate-to-Insulin Ratio
  • Hyperglycemia is a condition that exists when blood sugars are too high. While hyperglycemia is typically associated with diabetes, this condition can exist in many patients who do not have diabetes, yet have elevated blood sugar levels caused by trauma or stress from surgery and other complications from hospital procedures. Insulin therapy is used to bring blood sugar levels back into a normal range.
  • Hypoglycemia may occur at any time when a patient's blood glucose level is below a preferred target. Appropriate management of blood glucose levels for critically ill patients reduces co-morbidities and is associated with a decrease in infection rates, length of hospital stay, and death.
  • the treatment of hyperglycemia may differ depending on whether or not a patient has been diagnosed with DM1, DM2, gestational diabetes mellitus, or non-diabetic stress hyperglycemia.
  • the blood glucose target range BG TR is defined by a lower limit, i.e., a low target BG TRL and an upper limit, i.e., a high target BGTRH-
  • Diabetes Mellitus has been treated for many years with insulin. Some recurring terms and phrases are described below:
  • Injection Administering insulin by means of manual syringe or an insulin "pen,” with a portable syringe named for its resemblance to the familiar writing implement.
  • Infusion Administering insulin in a continuous manner by means of an insulin pump for subcutaneous insulin apparatus 123, 123a capable of continuous administration.
  • Basal-Bolus Therapy is a term that collectively refers to any insulin regimen involving basal insulin and boluses of insulin.
  • Basal Insulin Insulin that is intended to metabolize the glucose released by a patient's the liver during a fasting state. Basal insulin is administered in such a way that it maintains a background level of insulin in the patient's blood, which is generally steady but may be varied in a programmed manner by an insulin pump 123a. Basal insulin is a slow, relatively continuous supply of insulin throughout the day and night that provides the low, but present, insulin concentration necessary to balance glucose consumption (glucose uptake and oxidation) and glucose production (glucogenolysis and
  • Basal insulin needs are usually about 10 to 15 mU/kg/hr and account for 30% to 50% of the total daily insulin needs; however, considerable variation occurs based on the patient 10.
  • Bolus Insulin Insulin that is administered in discrete doses. There are two main types of boluses, Meal Bolus and Correction Bolus.
  • Meal Bolus Taken just before a meal in an amount which is proportional to the anticipated immediate effect of carbohydrates in the meal entering the blood directly from the digestive system.
  • the amounts of the Meal Boluses may be determined and prescribed by a physician 40 for each meal during the day, i.e., breakfast, lunch, and dinner.
  • the Meal Bolus may be calculated in an amount generally proportional to the number of grams of carbohydrates in the meal.
  • the amount of the Meal Bolus is calculated using a proportionality constant, which is a personalized number called the Carbohydrate-to-insulin Ratio (CIR) and calculated as follows:
  • CIR Carbohydrate-to-insulin Ratio
  • Meal Insulin Bolus ⁇ grams of carbohydrates in the meal ⁇ / CIR (1)
  • Correction Bolus CB Injected immediately after a blood glucose
  • the amount of the correction bolus is proportional to the error in the BG (i.e., the bolus is proportional to the difference between the blood glucose measurement BG and the patient's personalized Target blood glucose BGxarget)-
  • the proportionality constant is a personalized number called the Correction Factor, CF, and is calculated as follows:
  • a Correction Bolus CB is generally administered in a fasting state, after the previously consumed meal has been digested. This often coincides with the time just before the next meal.
  • blood glucose measurements BG are aggregated using an exponentially-weighted moving average EMA t as a function for each modal day's time interval BG.
  • the EMAt is calculated as follows:
  • n is the number of equivalent days averaged.
  • an arithmetic moving average is utilized that calculates the sum of all BG values in n days divided by a total count (n) of all values associated with the arithmetic average.
  • Basal -Bolus insulin therapy There are several kinds of Basal -Bolus insulin therapy including Insulin Pump therapy and Multiple Dose Injection therapy:
  • An insulin pump 123a is a medical device used for the administration of insulin in the treatment of diabetes mellitus, also known as continuous subcutaneous insulin infusion therapy.
  • the device includes: a pump, a disposable reservoir for insulin, and a disposable infusion set.
  • the pump 123a is an alternative to multiple daily injections of insulin by insulin syringe or an insulin pen and allows for intensive insulin therapy when used in conjunction with blood glucose monitoring and carbohydrate counting.
  • the insulin pump 123a is a battery-powered device about the size of a pager. It contains a cartridge of insulin, and it pumps the insulin into the patient via an "infusion set", which is a small plastic needle or "canula" fitted with an adhesive patch. Only rapid-acting insulin is used.
  • MDI Multiple Dose Injection
  • Rapid-acting insulins act on a time scale shorter than natural insulin. They are appropriate for boluses.
  • the clinical decision support system 100 includes a glycemic management module 50, an integration module 60, a surveillance module 70, and a reporting module 80.
  • Each module 50, 60, 70, 80 is in communication with the other modules 50, 60, 70, 80 via a network 20.
  • the network 24 (discussed below) provides access to cloud computing resources that allows for the performance of services on remote devices instead of the specific modules 50, 60, 70, 80.
  • the glycemic management module 50 executes a process 200 (e.g., an executable instruction set) on a processor 112, 132, 142 or on the cloud computing resources.
  • the integration module 60 allows for the interaction of users 40 and patients 10 with the system 100.
  • the integration module 60 receives information inputted by a user 40 and allows the user 40 to retrieve previously inputted information stored on a storage system (e.g., one or more of cloud storage resources 24, a non-transitory memory 144 of a clinic's electronic medical system 140, a non-transitory memory 114 of the patient device 110, or other non-transitory storage media in communication with the integration module 60). Therefore, the integration module 60 allows for the interaction between the users 40, patients 10, and the system 100 via a display 116, 146. In some examples, integration module 60 allows the user 40 or patient 10 to input blood glucose history data 208b associated with the patient 10 for storage on the storage system 24, 144, 114.
  • a storage system e.g., one or more of cloud storage resources 24, a non-transitory memory 144 of a clinic's electronic medical system 140, a non-transitory memory 114 of the patient device 110, or other non-transitory storage media in communication with the integration module 60. Therefore, the integration module 60 allows for the
  • the surveillance module 70 considers patient state information 208a received from a user 40 via the integration module 60 and information received from a glucometer 124 that measures a patient's blood glucose value BG and determines if the patient 10 is within a threshold blood glucose value BG TH . In some examples, the surveillance module 70 alerts the user 40 if a patient's blood glucose values BG are not within a threshold blood glucose value BG-m.
  • the surveillance module 70 may be preconfigured to alert the user 40 of other discrepancies between expected values and actual values based on pre-configured parameters
  • the reporting module 80 may be in communication with at least one display 116, 146 and provides information to the user 40 determined using the glycemic management module 50, the integration module 60, and/or the surveillance module 70. In some examples, the reporting module 80 provides a report that may be displayed on a display 116, 146 and/or is capable of being printed.
  • the system 100 is configured to evaluate a glucose level and nutritional intake of a patient 10. Based on the evaluation and analysis of the data, the system 100 calculates an insulin dose, which is administered to the patient 10 to bring and maintain the blood glucose level of the patient 10 into the blood glucose target range BG TR .
  • the system 100 may be applied to various devices, including, but not limited to, subcutaneous insulin infusion pumps 123a, insulin pens 123b, glucometers 124, continuous glucose monitoring systems, and glucose sensors.
  • the system 100 considers outcome data associated with an insulin dose administered to the patient 10.
  • the outcome data may include a next scheduled blood glucose measurement BGnext showing the effect of the insulin dose previously administered to the patient 10.
  • the BGnext occurs a sufficient amount of time (e.g., four to six hours) after the insulin dose is administered to the patient after the effects of the insulin and food are both complete so that the BGnext indicates the precision of the recommended dose.
  • the system 100 may adjust a next recommended insulin dose by increasing the dose when the BGnext is greater than a target center BG T C of the blood glucose target range BG TR , or decreasing the dose when the BGnext is less than the target center BG T c-
  • the next recommended insulin dose may include a next scheduled meal bolus after the administered insulin dose or a meal bolus associated with the administered insulin dose, but on the next day.
  • the clinical decision support system 100 includes a network 20, a patient device 110, a dosing controller 160, a service provider 130, and a meter manufacturer provider 160.
  • the patient device 110 may include, but is not limited to, desktop computers 110a or portable electronic device 110b (e.g., cellular phone, smartphone, personal digital assistant, barcode reader, personal computer, or a wireless pad) or any other electronic device capable of sending and receiving information via the network 20.
  • one or more of the patient's glucometer 124, insulin pump 123a, or insulin pen 123b are capable of sending and receiving information via the network 20.
  • the patient device 110a, 110b includes a data processor 112a, 112b (e.g., a computing device that executes instructions), and non-transitory memory 114a, 114b and a display 116a, 116b (e.g., touch display or non-touch display) in communication with the data processor 112.
  • the patient device 110 includes a keyboard 118, speakers 212, microphones, mouse, and a camera.
  • the glucometer 124, , insulin pump 123a, and insulin pen 123b associated with the patient 10 include a data processor 112c, 112d, 112e (e.g., a computing device that executes instructions), and non-transitory memory 114c, 114d, 114e and a display 116c, 116d, 116e (e.g., touch display or non-touch display in communication with the data processor 112c, 112d, 112e.
  • the meter manufacturer provider 190 may include may include a data processor 192 in communication with non-transitory memory 194.
  • the data processor 192 may execute a proprietary download program 196 for downloading blood glucose BG data from the memory 114c of the patient's glucometer 124.
  • the proprietary download program 196 is implemented on the health care provider's 140 computing device 142 or the patient's 10 device 110a for
  • the download program 196 exports a BG data file for storage in the non-transitory memory 24, 114, 144.
  • the data processor 192 may further execute a web-based application 198 for receiving and formatting BG data transmitted from one or more of the patient's devices 110a, 110b, 124, 123a, 123b and storing the BG data in non-transitory memory 24, 114, 144.
  • the service provider 130 may include a data processor 132 in communication with non-transitory memory 134.
  • the service provider 130 provides the patient 10 with a process 200 (see FIG. 2 A) (e.g., a mobile application, a web-site application, or a downloadable program that includes a set of instructions) executable on a processor 112, 132, 142, 192 of the dosing controller 160 and accessible through the network 20 via the patient device 110, health care provider electronic medical record systems 140, portable blood glucose measurement devices 124 (e.g., glucose meter or glucometer), or portable administration devices 123a, 123b.
  • a process 200 e.g., a mobile application, a web-site application, or a downloadable program that includes a set of instructions
  • portable blood glucose measurement devices 124 e.g., glucose meter or glucometer
  • portable administration devices 123a, 123b e.g., glucose meter or glucometer
  • a health care provider medical record system 140 is located at a clinic 42 (or a doctor's office) and includes a data processor 142, a non- transitory memory 144, and a display 146 (e.g., touch display or non-touch display).
  • the transitory memory 144 and the display 146 are in communication with the data processor 142.
  • the health care provider electronic medical system 140 includes a keyboard 148 in communication with the data processor 142 to allow a user 40 to input data, such as patient-state information 208a (FIGS.2A-2C).
  • the non-transitory memory 144 maintains patient records capable of being retrieved, viewed, and, in some examples, modified and updated by authorized hospital personal on the display 146.
  • the dosing controller 160 is in communication with the glucometer 124, insulin administration device 123a, 123b and includes a computing device 112, 132, 142 and non-transitory memory 114, 134, 144 in communication with the computing device 112, 132, 142.
  • the dosing controller 160 executes the process 200.
  • the dosing controller 160 stores patient related information retrieved from the glucometer 124 to determine an insulin dose rate IRR based on the received blood glucose measurement
  • the dosing controller 160 may store blood glucose BG history data 208b associated with the patient 10 that includes treatment doses and outcome attributes associated with each treatment dose administered by the patient.
  • the treatment dose may include the insulin dose rate IRR administered to the patient 10 and the outcome attribute may include the BGnext associated with the administered IRR.
  • the treatment dose may include one or more ADMs administered to the patient 10 and the outcome attribute may include a next scheduled Ale level associated with the
  • the insulin device 123 in communication with the dosing controller 160, is capable of executing instructions for administering insulin according to a subcutaneous insulin treatment program selected by the dosing controller 160.
  • the administration device 123 may include the insulin pump 123a or the pen 123b.
  • the administration device 123 is in communication with the glucometer 124 and includes a computing device 112d, 112e and non-transitory memory 114d, 114e in communication with the computing device 112d, 112e.
  • the administration device 123 includes a doser 223a, 223b in communication with the administration computing device 112d, 112e for administering insulin to the patient 10.
  • the doser 223a of the insulin pump 123a includes an infusion set including a tube in fluid communication with an insulin reservoir and a cannula inserted into the patient's 10 body and secured via an adhesive patch.
  • the doser 223b of the pen 123b includes a needle for insertion into the patients 10 for administering insulin from an insulin cartridge.
  • the administration device 123 may receive a subcutaneous insulin treatment program selected by and transmitted from the dosing controller 160, while the administration computing device 112d, 112e may execute the subcutaneous insulin treatment program.
  • the dosing controller 160 executes on the administration computing device 112d, 112e.
  • Executing the subcutaneous insulin treatment program by the administration computing device 112d, 112e causes the doser 223a, 223b to administer doses of insulin specified by the subcutaneous insulin treatment program. For instance, units for the doses of insulin may be automatically set or dialed in by the administration device 123a, 123b and administered via the doser 223a, 223b to the patient 10. Accordingly, the administration devices 123a, 123b may be "smart" administration devices capable of communicating with the dosing controller 160, or implementing the dosing controller 160, to populate recommended doses of insulin for administering to the patient 10.
  • the network 20 may include any type of network that allows sending and receiving communication signals, such as a wireless telecommunication network, a cellular telephone network, a time division multiple access (TDMA) network, a code division multiple access (CDMA) network, Global system for mobile communications (GSM), a third generation (3G) network, fourth generation (4G) network, a satellite communications network, and other communication networks.
  • the network 20 may include one or more of a Wide Area Network (WAN), a Local Area Network (LAN), and a Personal Area Network (PAN).
  • the network 20 includes a combination of data networks, telecommunication networks, and a combination of data and telecommunication networks.
  • the patient device 110, the service provider 130, and the hospital electronic medical record system 140 communicate with each other by sending and receiving signals (wired or wireless) via the network 20.
  • the network 20 provides access to cloud computing resources, which may be elastic/on- demand computing and/or storage resources 24 available over the network 20.
  • cloud' services generally refers to a service performed not locally on a user's device, but rather delivered from one or more remote devices accessible via one or more networks 20.
  • the process 200 receives parameters (e.g., patient condition parameters) inputted via the client device 110, the service provider 130, and/or the clinic system 140, analyzes the inputted parameters and starts a patient treatment program 300b (FIG. 3B).
  • FIG. 2D shows a start mode selector 301 for starting a training program 300a that may be run at calendar intervals.
  • the training program 300a enables the program to train itself to perform more effectively. To accomplish this, it retrieves a set of new training data 308 from the BG history data 208b and processes it to learn an up-to-date predictive model capable of predicting glycemic variables for a wide number of patient-profiles.
  • the training program 300a uses patient-state information 208a, BG history data 208b, and/or SubQ information 208c to tabulate counts and calculate probabilities, averages, regression functions, and/or other statistical data that may be saved (in the non-transitory memory 114, 134, 144).
  • the probabilities and other statistical data are available for use by the treatment program 300b to predict an optimum treatment dose for the patient 10 that will yield a favorable outcome attribute.
  • the treatment program 300b may adjust a recommended dose of insulin for a SubQ meal bolus adjustment program 400a (FIG. 4A) or a SubQ basal adjustment program 400b (FIG. 4B) to bring and maintain a patient's blood glucose level BG as close to a target center BG T C of a preferred target range BG TR .
  • the process 200 may receive a username and a password (e.g., at a login screen displayed on the display 116, 146) to verify that a qualified and trained healthcare professional 40 is initiating the process 200 and entering the correct information that the process 200 needs to accurately administer insulin to the patient 10.
  • the system 100 may customize the login screen to allow a user 40 to reset their password and/or username.
  • the system 100 may provide a logout button (not shown) that allows the user 40 to log out of the system 100. The logout button may be displayed on the display 116, 146 at any time during the execution of the process 200.
  • the clinical decision support system 100 may include an alarm system 120 that alerts a user 40 when the patient's blood glucose level BG is outside the target range BG TR .
  • the alarm system 120 may produce an audible sound via speaker 122 in the form of a beep or some like audio sounding mechanism.
  • the alarm system 120 displays a warning message or other type of indication on the display 116a-e of the patient device 110 to provide a warning message.
  • the alarm system 120 may also send the audible and/or visual notification via the network 20 to the clinic system 140 (or any other remote station) for display on the display 146 of the clinic system 140 or played through speakers 152 of the clinic system 140.
  • the process prompts a user 40 to input patient information 208a-c at block 208.
  • the user 40 may input the patient information 208a-c, for example, via the user device 110 or via the health care provider medical record system 140 located at a clinic 42 (or a doctor's office).
  • the user 40 may input new patient information 208a-c as shown in FIG. 2B.
  • the process 200 may retrieve the patient information 208a-c from the non- transitory memory 144 of the clinic's electronic medical system 140 or the non-transitory memory 114 of the patient device 110 (e.g., where the patient information 208a-c was previously entered and stored).
  • the patient information 208a-c may include, but is not limited to, patient-state information 208a, BG history data 208b, and SubQ information 208c associated with the patient 10.
  • the patient-state information 208a for the patient 10 may include one or more patient-state attributes associated with the patient 10.
  • the patient-state attributes are associated with attributes that do not change, change slowly, or change infrequently.
  • the patient-state information 208a may include, but is not limited to a patient's name, a patient's identification number (ID), a patient's height, weight, date of birth, diabetes history, disease history, clinical attributes, financial attributes, and any other relevant information.
  • the disease history may include a list of all diseases of the patient 10 and a list of all medications prescribed for treating those diseases. Information in the disease history may indicate whether the patient 10 has important comorbidities that may dictate the personalized diabetes treatment therapy prescribed to the patient 10.
  • the clinical attributes may include all of the patient's medical providers and a record of pertinent information relating to past clinical visits.
  • the clinical attributes may include symptoms and/or test results associated with the patient 10.
  • symptoms and/or test results may indicate whether or not the patient 10 has established vascular complications.
  • Financial attributes may include insurance coverage, salary, and/or education of the patient 10 for considering when prescribing a particular medication.
  • medications that are not covered by a patient's insurance plan may be difficult prescriptions for the patient to sustain, and therefore, alternative medications may need to be considered.
  • the other relevant patient- state information may include, but is not limited to, a life expectancy of the patient, important comorbidities, established vascular complications, whether the patient's resources and support system are readily available or limited, and patient attitude.
  • the patient attitude may indicate whether the patient 10 is highly motivated and adherent with excellent self-care capacities, or whether the patient 10 is less motivated and non-adherent with poor self-care capabilities.
  • the BG history data 208b includes treatment doses and outcome attributes associated with each treatment dose administered by the patient.
  • FIG. 2B shows the display 116, 146 prompting the user 40 with the option to manually input the BG history data 208b of the patient 10 upon selection of a "Manual" button or to download the BG history data 208b upon selection of a "Download” button.
  • the patient's 10 smartphone 110b or tablet may communicate with the glucometer 124 and/or the insulin administration devices 123a, 123b via Bluetooth or other connection to download the BG history data 208b from the memory 114c of the glucometer 124, and transmit the downloaded BG history data 208b to the dosing controller 160 through the network 20.
  • the glucometer 124 may communicate directly with the dosing controller 160 to transmit the BG history data 208b from the memory 1 14c of the glucometer 124 to the dosing controller 160 through the network 20.
  • FIG. 2C shows the display 116, 146 displaying the BG history data 208b as a chronological record of each insulin dose administered to the patient 10 and the outcome history indicating the BGnext occurring after each insulin dose.
  • the BGnext may correspond to a next scheduled BG measurement that occurs at a meal time after administering a meal bolus for a previous meal.
  • the BGnext associated with a breakfast bolus occurs at a pre-lunch time
  • the BGnext associated with a lunch bolus occurs at a pre- dinner time
  • the BG next associated with a dinner bolus occurs at bedtime or at the next day's pre-breakfast time.
  • the process 200 at block 208 requests the user 40 to enter SubQ information 208c for the patient 10, such as patient diabetes status, subcutaneous type ordered for the patient 10 (e.g., Basal/bolus and correction that is intended for patients on a consistent carbohydrate diet, total daily dosage (TDD), bolus insulin type (e.g., Novolog), basil insulin type (e.g., Lantus) and frequency of distribution (e.g., 1 dose per day, 2 doses per day, 3 doses per day, etc.), basil time, basal percentage of TDD, meal bolus percentage of TDD , daily meal bolus distribution (e.g., breakfast bolus, lunch bolus and dinner bolus), or any other relevant information.
  • TDD is calculated in accordance with equation:
  • TDD QuickTransitionConstant * M Trans (4A) where QuickTransitionConstant is usually equal to 1000, and M Tra ns is the patient's multiplier at the time of initiation of the SubQ transition process.
  • the TDD is calculated by a statistical correlation of TDD as a function of body weight.
  • the following equation is the correlation used:
  • the patient's total daily dose TDD is calculated in accordance with the following equation:
  • M Trans is the patient's multiplier at the time of initiation of a SubQ transition process.
  • the patient SubQ information 208c is prepopulated with default parameters, which may be adjusted or modified. In some examples, portions of the patient SubQ information 208c are prepopulated with previously entered patient subcutaneous information 208c.
  • the process 200 may prompt the request to the user 40 to enter the SubQ information 208c on the display 116 of the patient device 110. In some implementations, the process 200 prompts the request on the display 116 for a custom start of new patients (FIG. 2B) undertaking the training program 300a or the treatment program 300b.
  • the user 40 may enter SubQ information 208c including the patient's 10 correction factor CF (e.g., 1700) and target BG range for calculating the correction bolus CB using EQ. 2. As shown in FIG. 2B, the user 40 may enter an Insulin- to-Carbohydrate Ratio (ICR) for determining a recommended insulin dose based on a number of carbohydrates that the patient 10 consumes at an associated meal.
  • ICR Insulin- to-Carbohydrate Ratio
  • the display 116, 146 may show the patient-state information 208a, the BG history data 208b, and/or the SubQ information 208c for an existing or returning patient 10.
  • the patient information 208a-c may be retrieved from the non-transitory memory 24, 114, 124 of the system 100 and displayed upon the display 116, 146 by entering the patient's 10 name and/or
  • FIG. 2C the process 200 allows the user 40 to start the patient treatment program 300b.
  • FIGS. 2B and 2C allow the user 40 to start the patient treatment program 300b by selecting a "Treatment" button upon the display 116, 146.
  • the process 200 may allow the user 40 to determine a frequency and/or date- range of training data for use by the training program 300a (FIG. 3 A).
  • FIG. 2D shows an exemplary start mode selector 301 for the training program 300a that allows the user 40 to select one of an automatic start 303 of the training program 300a, an immediate start 305 of the training program 300a, or a clean re-start 307 of the training program 300a.
  • the automatic start 303 may be a default setting for the training program 300a and may be configurable at an interval of Ndays. In some examples, the value for Ndays is equal to 60 days. However, the user 40 may desire an early or un-scheduled start of the training program 300a by selecting the immediate start 305.
  • selection of the clean re-start 307 re-initializes self-learning memories of the training program 300a (e.g., stored in non-transitory memory 24, 114, 124).
  • the user 40 may select the clean restart when changes to the training program 300a have been implemented or changes will occur at a known date. Accordingly, the user 40 may select a start of the data date-range via a calendar pull-down button, as shown on the display 116, 146 of FIG. 2D.
  • the training program 300a commences based on the selected user input to the start mode selector 301 of FIG. 2D. For instance, the training program 300a may start at block 302 in response to a selection of one of the automatic start 303, the immediate start 305, or the clean re-start 307 at the start mode selector 301 of FIG. 2D.
  • the clean re-start 307 may be accompanied by a custom input start date and used when changes to the training program 300a result in previously obtained data to now be obsolete.
  • FIG. 3 A shows an overview of the training program 300a operating in the automatic start 303 with a time period of Ndays (e.g., 60days).
  • a process timer 306 may initiate to count the time period.
  • the training program 300a is a periodic optimization process that may improve an insulin dose- advising program for treating diabetic patients.
  • the training program 300a may apply to a SubQ meal bolus adjustment program 400a (FIG. 4A) and a SubQ basal adjustment program 400b (FIG. 4B).
  • Each of the programs 400a, 400b may include decision trees for adjusting recommended insulin doses for meal bolus or basal, respectively.
  • the training program 300a obtains the BG history data 208b associated with the patient 10 and provides the BG history data 208b as new training data at block 310.
  • the new training data at block 310 also includes the patient's patient-state information 208a and the patient's SubQ information 208c.
  • only the BG history data 208b since the last cycle of the training program 300a is considered for use in the instant training program 300a.
  • the new data may be added by default to the old data already stored in the training program 300a memory (non-transitory memory 24, 114, 124).
  • the user 40 may override the default and direct a re-initialization of the memories in the training program 300a via selection of the clean re-start 307 in the start mode selector 301 of FIG. 2D.
  • the training program processes the new training data at block 310 to obtain counts and calculate probabilities in the manner of tree-training based on the patient's patient-state information 208a, BG history data 208b, and/or SubQ information 208c.
  • the training program 300a may obtain the new training data at block 310 chronologically one dose adjustment (e.g., bolus or basal) at a time.
  • the BG history data 208b may include a calculation of an insulin dose administered to the patient 10 and the outcome data associated with the insulin dose, obtained at a time when the next scheduled blood glucose measurement BGnext shows the result of the administered insulin dose.
  • each patient-state attribute PA, PAl-PAj of the patient's patient-state information 208a is provided to block 314.
  • a tree connector for each patient-state attribute PA may extend from block 312 for input in block 314.
  • block 314 corresponds to a parent box associated with all the patient-state attributes PA of the patient 10 and collectively includes one or more child boxes each pertaining to respective ones of the patient-state attributes PA.
  • the training program 300a may increment counts for each patient-state attribute during each dose adjustment, and calculate probabilities for each patient-state attribute. Accordingly, the training program 300a may designate an appropriate box for each patient-state attribute of the patient 10 one dose adjustment at a time.
  • the training program 300a builds a classifier for a patient population that may be used to predict group attributes of new cases from the domain knowledge base based on values of other attributes. While FIG. 3 A shows the training program 300a using the decision tree for classifying attributes, the training program 300a may also use other classification methods such as IF-Then Rule Induction, Bayesian Classifiers, Naive Baysian Classifiers, Iterative Dichotomiser 3 (TD3) algorithms, K Nearest Neighbor, and Neural Networks.
  • the training program 300a processes the patient-state information 208a, the BG history data 208b, and the SubQ information 208c to calculate the adjusted insulin dose for the patient 10, and subsequently recommend the adjusted insulin dose to the patient 10.
  • the dosing controller 160 may transmit the adjusted insulin dose to the patient computing device 110.
  • the dosing controller 160 transmits the adjusted insulin dose calculated at block 316 to the administration device 123a, 123b and the doser 223a, 223b administers the insulin to the patient 10.
  • Block 316 may include each adjusted insulin dose TA, TAl-TAj during the time period of Ndays selected for the training program.
  • the training program 300a at block 316, may increment counts for each adjusted insulin dose, and calculate
  • the training program 300a obtains an outcome attribute associated with the adjusted insulin dose calculated and administered at block 316.
  • Block 318 may include outcome attributes OA, OAl-OAi associated with each of the adjusted insulin doses TA, TAl-TAj administered by the patient 10 at block 316.
  • the training program 300a, at block 318, may increment counts for each outcome attribute, and calculate probabilities for each of the outcome attributes.
  • the outcome attribute may include the BGnext occurring a sufficient period of time (e.g., four to six hours) after the adjusted dose is administered by the patient.
  • the training program 300a calculates an evaluation or a "grade" on the adjusted dose administered by the patient 10 at block 316.
  • the BGnext may be further processed into another outcome attribute, BG Percent Error (Err%), calculated in accordance with the following equation:
  • the training program 300a may average the BGnext (MeanBGnext) and the Err% (MeanErr%) for each adjusted insulin dose and store the values in a child box contained by a parent box corresponding to the adjusted meal bolus dose. Accordingly, the training program 300a calculates the evaluation or "grade" on each adjusted meal bolus dose based on one or more outcome attributes associated with the adjusted meal bolus dose. Here, adjusted meal bolus doses of insulin resulting in favorable outcome attributes are assigned higher "grades" than those resulting in less favorable outcome attributes. Thus, the training program 300a may identify a best or optimum treatment dose of insulin (e.g., best or optimum meal bolus dose) from one of the adjusted meal bolus doses of insulin administered by the patient that yields the most favorable outcome attribute.
  • a best or optimum treatment dose of insulin e.g., best or optimum meal bolus dose
  • the outcome attribute at block 318 may include a next scheduled breakfast BG measurement
  • BGbreakfastNext occurring after the adjusted basal dose is administered at block 316.
  • the BGbreakfastNext is obtained after the patient 10 has fasted during sleep to accurately show how well the adjusted basal dose controlled the patient's blood glucose levels.
  • the training program 300a calculates an evaluation or a "grade" on the adjusted dose administered by the patient 10 at block 316.
  • the BGbreakfastNext may be further processed into another outcome attribute, BGbreakfast Percent Error (BrkErr%), calculated in accordance with the following equation:
  • BrkErr% ABS[(BGBreakfastNext-BG TC ) / BG TC ] (6)
  • the training program 300a may average the BrkErr% (MeanBrkErr%) for each adjusted insulin dose and store the values in a child box contained by a parent box associated with the adjusted dose corresponding to the adjusted basal dose. Accordingly, the training program 300a calculates the evaluation or "grade” on each adjusted basal dose based on one or more outcome attributes associated with the adjusted basal dose. Here, adjusted basal doses of insulin resulting in favorable outcome attributes are assigned higher "grades" than those resulting in less favorable outcome attributes. Thus, the training program 300a may identify a best or optimum treatment dose of insulin (e.g., best or optimum basal dose) from one of the adjusted basal doses of insulin administered by the patient that yields the most favorable outcome attribute.
  • a best or optimum treatment dose of insulin e.g., best or optimum basal dose
  • the training program 300a may proceed back to block 302 and obtain the BG history data 208b at block 308 for another dose-adjustment history. Upon completing the training program 300a for each insulin dose to be adjusted, the training program 300a stops the iterative process of incrementing the counts and calculating the probabilities within the non-transitory memory 24, 114, 124 at each of blocks 314, 316, 318. The training program 300a may build the decision tree for a patient population based on the patient-state information 208a, BG history data 208b, and the SubQ information 208c for each patient of the patient population.
  • the decision tree may be retrieved from the non-transitory memory 24, 114, 124 for use by the patient treatment program 300b to allow the user 40 (e.g., physician or medical professional) to prescribe a personalized patient treatment therapy for a patient 10.
  • the treatment program 300b uses the training data contained in the decision tree processed by the training program 300a for determining an optimum insulin dose and recommending the optimum insulin dose to a patient in treatment.
  • the treatment program 300b commences at block 330 with a new patient or a returning patient.
  • the treatment program obtains the patient information associated with the new or returning patient.
  • the patient information may include patient-state information 208a, BG history data 208b, and SubQ information 208c.
  • each patient-state attribute PA, PAl-PAj of the patient's patient-state information 208a is provided to block 314.
  • a tree connector for each patient-state attribute PA may extend from block 312 for input in block 314.
  • block 314 corresponds to a parent box associated with all the patient-state attributes PA of the patient 10 and collectively includes one or more child boxes each pertaining to respective ones of the patient-state attributes PA.
  • the treatment program 300b may assign each patient-state attribute of the patient 10 into a corresponding box associated with the training program 300a of FIG. 3A. The treatment program 300b, however, does not increment counts or calculate probabilities for the patient-state attributes associated with the patient 10 being treated.
  • the treatment program 300b processes the patient-state information 208a, the BG history data 208b, and the SubQ information 208c to determine a recommended insulin dose RA1, RA2-RAj for the patient 10. However, rather than providing the recommended insulin dose for the patient 10 to administer, the treatment program 300b, at block 338, compares the recommended insulin dose with the adjusted insulin doses TA, TAl-TAj and the associated outcome attributes OA, OAl-OAi obtained from the training program 300a.
  • the adjusted insulin doses TA, TAl-TAj calculated in block 316 of the training program 300a and the associated outcome attributes OA, OAl-OAi obtained from blocks 318 and 338, respectively, of the training program 300a may only correspond to patients of the patient population that have the same diagnosis and similar patient-state attributes as the patient 10 being treated.
  • the treatment program 300b may determine (e.g., predict) an optimum insulin dose for the patient 10 that will yield a most favorable outcome attribute based on the comparison with the adjusted insulin doses and associated outcome attributes obtained from the training program 300a.
  • the optimum insulin dose for the patient 10 corresponds to the best treatment dose of insulin identified at block 318 of the patient training program 300a for one or more patients of the patient population that have similar patient-state attributes as the patient 10 being treated.
  • Block 340 may provide the optimum insulin dose for the patient 10 to block 336 to replace the insulin dose previously recommended.
  • the reporting module 80 of the system 100 may recommend the optimum insulin to the patient 10 by transmitting the optimum insulin dose to the patient computing device 110 for the patient 10 to view upon the display 116, transmitting an email containing the optimum insulin dose to the patient 10, and/or printing the optimum insulin dose in a report for the patient 10.
  • the dosing controller 160 executing the treatment program 300b may transmit the optimum insulin dose to the administration device 123 (e.g., pump 123a or smart pen 123b) so that the administration computing device 112d, 112e may instruct the doser 223a, 223b to administer the optimum insulin dose to the patient 10.
  • the administration device 123 e.g., pump 123a or smart pen 123b
  • an anti-diabetes program 300c prescribes ADM dose-combinations to a patient 10 for delaying, and in some instances eliminating, the progression of diabetes.
  • FIG. 3C provides an overview combining the training and treatment programs 300a, 300b, respectively, for adjusting ADMs. Accordingly, the present disclosure may refer to the program 300c as an anti- diabetes program or an ADM program.
  • the program 300c may operate in a training mode or in a treatment mode.
  • the training mode of the program 300c includes a periodic optimization process that may improve a non-insulin dose-advising program for treating patients with a high probability of becoming diabetic or for treating patients diagnosed with Type 2 diabetes mellitus (DM2).
  • DM2 Type 2 diabetes mellitus
  • the anti-diabetes program may apply to an ADM adjustment program 400c (FIG. 4C) that may include decision tress for adjusting recommended doses of one or more ADMs.
  • the training mode of the anti-diabetes program 300c may start in response to a selection of one of the automatic start 303, the immediate start 305, or the clean re-start 307 at the start mode selector 301 of FIG. 2D.
  • the clean re-start 307 may be accompanied by a custom input start date and used when changes to the anti-diabetes program 300c result in previously obtained data to now be obsolete.
  • FIG. 3C shows the anti-diabetes program 300c operating in the automatic start 303 with a time period of Ndays (e.g., 60days).
  • a process timer 356 may initiate to count the time period.
  • the training mode of the program 300c obtains the BG history data 208b associated with the patient 10 and provides the BG history data 208b as new training data at block 360.
  • the new training data at block 360 may also include the patient's patient-state information 208a.
  • only the BG history data 208b since the last cycle of the training mode of the program 300a is considered for use in the instant training mode cycle.
  • the new data may be added by default to the old data already stored in the training program 300a memory (non-transitory memory 24, 114, 124).
  • the user 40 may override the default and direct a re-initialization of the memories in the program 300c via selection of the clean re-start 307 in the start mode selector 301 of FIG. 2D.
  • the anti-diabetes program 300c processes the new training data at block 360 to obtain counts and calculate probabilities in the manner of tree-training based on the patient's patient-state information 208a and the BG history data 208b.
  • the training mode of the program 300c may obtain the new training data at block 360 chronologically one ADM dose adjustment at a time.
  • the BG history data 208b may include a calculation of one or more ADM dose-combinations administered by the patient 10 and the outcome data corresponding to an Ale level associated with the one or more ADM doses administered by the patient 10. Additionally or alternatively, the outcome data may include a next scheduled blood glucose measurement BGnext occurring a sufficient time after the patient 10 administers the ADM dose-combination, and thereby showing the glycemic result of the ADM dose-combination.
  • each patient-state attribute PA, PAl-PAj of the patient' s patient-state information 208a is provided to block 364.
  • a tree connector for each patient-state attribute PA may extend from block 362 for input in block 364.
  • block 364 corresponds to a parent box associated with all the patient-state attributes PA of the patient 10 and collectively includes one or more child boxes each pertaining to respective ones of the patient-state attributes PA.
  • the anti-diabetes program 300c at block 364, may increment counts for each patient-state attribute during each dose adjustment, and calculate probabilities for each patient-state attribute.
  • the training mode of the program 300c may designate an appropriate box for each patient-state attribute of the patient 10 one dose adjustment at a time.
  • the training mode of the program 300c builds a classifier for a patient population that may be used to predict group attributes of new cases from the domain knowledge base based on values of other attributes.
  • FIG. 3C shows the anti-diabetes program 300c using the decision tree for classifying attributes
  • the anti- diabetes program 300c may also use other classification methods such as IF-Then Rule Induction, Bayesian Classifiers, Naive Baysian Classifiers, Iterative Dichotomiser 3 (ID3) algorithms, K Nearest Neighbor, and Neural Networks.
  • the anti-diabetes program 300c processes the patient-state information 208a, the BG history data 208b, and the SubQ information 208c to calculate the adjusted ADM dose-combination for the patient 10 and subsequently recommends the adjusted ADM dose-combination to the patient 10.
  • Block 366 may include each ADM dose-combination TA, TAl-TAj and corresponding ADMs ADM1, ADM2-ADMk during the time period of Ndays selected for the training mode of the anti-diabetes program 300c.
  • the anti-diabetes program 300c at block 368, may increment counts for each ADM dose-combination, and calculate probabilities for each of the ADM dose- combinations during the training mode. During the treatment mode, however, block 366 does not increment counts or calculate probabilities.
  • Block 368 the program 300c obtains an outcome attribute associated with the ADM dose-combinations calculated and administered at block 366.
  • Block 368 may include outcome attributes OA, OAl-OAi associated with each of the ADM dose-combination TA, TAl-TAj administered by the patient 10 at block 366.
  • the training mode of the anti-diabetes program 300c, at block 368 may increment counts for each outcome attribute, and calculate probabilities for each of the outcome attributes. During the treatment mode, however, the anti-diabetes program 300c does not increment counts or calculate probabilities at block 368.
  • the outcome attribute may include a next scheduled Ale level (AlcNext) occurring after the ADM dose-combination is administered by the patient.
  • AlcNext is obtained by the anti-diabetes program 300c within two to three months after the ADM dose-combination is administered by the patient 10.
  • the anti-diabetes program 300c calculates an evaluation or a "grade" on the ADM dose-combination administered by the patient 10 at block 366.
  • the anti-diabetes program 300c may average the AlcNext (MeanAlcNext) for each of the ADM dose-combination administered by the patient 10 and store the values in a child box contained by a parent box associated with ADM dose- combinations.
  • the training mode of the anti-diabetes program 300c may proceed back to block 352 and obtain the BG history data 208b at block 358 for another dose-adjustment history.
  • the training mode ends and the anti-diabetes program 300c stops the iterative process of incrementing the counts and calculating the probabilities within the non-transitory memory 24, 114, 124 at each of blocks 364, 366, 368.
  • the anti-diabetes program 300c may process a decision tree during the training mode for a patient population based on the patient-state information 208a and the BG history data 208b for each patient of the patient population.
  • the anti-diabetes program 300c uses the training data contained in the decision tree processed during the training mode for determining an optimum ADM dose-combination and recommending the optimum ADM dose- combination to a patient 10 in treatment.
  • the treatment mode may commence at block
  • the treatment mode of the anti- diabetes program 300c may assign each patient-state attribute of the new or returning patient 10 into a corresponding box at block 364. However, the treatment mode does not increment counts or calculate probabilities for the patient-state attributes associated with the patient 10 being treated.
  • the anti-diabetes program 300c processes the patient-state information 208a and the BG history data 208b and calculates a
  • the treatment mode of the program 300c compares the recommended ADM dose- combination with the adjusted ADM dose-combinations TA, TAl-TAj and associated outcome attributes OA, OA1, OAi obtained from the training data during the training mode.
  • the anti-diabetes program 300c may compare the recommended ADM dose- combination with the adjusted ADM dose-combinations TA, TAl-TAj and associated outcome attributes OA, OAI, OAi that only correspond to patients of the patient population that have the same diagnosis and similar patient-state attributes as the patient 10 being treated.
  • the treatment mode of the program 300c may determine an optimum ADM (insulin or non-insulin) dose-combination for the patient 10 that that will yield a most favorable outcome attribute based on the adjusted ADM dose- combinations and associated outcome attributes obtained from the training data.
  • Block 370 may provide the optimum ADM dose-combination to block 366 to replace the ADM dose-combination previously recommended.
  • the reporting module 80 of the system 100 may recommend the optimum ADM dose-combination to the patient 10 by transmitting the optimum ADM dose-combination to the patient computing device 110 for the patient 10 to view upon the display 116, transmitting an email containing the optimum ADM dose-combination to the patient 10, and/or printing the optimum ADM dose-combination in a report for the patient 10.
  • a SubQ meal bolus adjustment program 400a may operate in a training mode associated with the training program 300a (FIG. 3A) and a treatment mode associated with the treatment program 300b (FIG. 3B).
  • FIG. 4A provides details of training and treatment for adjusting meal boluses.
  • the SubQ meal bolus adjustment program 400a may execute on a computing device, such as service provider data processing hardware 130, 160, a cloud resource, or some other computing device 112, 132, 142.
  • the training mode of the SubQ meal bolus adjustment program 400a may start at block 402 in response to a selection of one of the automatic start 303, the immediate start 305, or the clean re-start 307 at the start mode selector 301 of FIG. 2D.
  • the clean re-start 307 may be accompanied by a custom input start date and used when changes to the training program 300a result in previously obtained data to now be obsolete.
  • FIG. 4A shows the training mode of program 400a operating in the automatic start 303 with a time period of Ndays (e.g., 60days).
  • a process timer 406 may initiate to count the time period.
  • the training mode is a periodic optimization process that may improve the meal bolus dose recommendations determined by the SubQ meal bolus adjustment program 400a.
  • the treatment mode of the SubQ meal bolus adjustment program 400a obtains patient information including the BG history data 208b associated with the patient 10 and provides the BG history data 208b as new training data at block 410.
  • the new training data at block 410 also includes the patient's patient-state information 208a and the patient's SubQ information 208c.
  • the SubQ meal bolus adjustment program 400a may obtain the new training data at block 410 chronologically one meal bolus dose adjustment at a time.
  • the BG history data 208b may include a calculation of an insulin dose (e.g., meal bolus) administered by the patient 10 and the outcome data associated with the insulin dose, obtained at a time when the next scheduled blood glucose measurement BGnext shows the result of the administered insulin dose.
  • an insulin dose e.g., meal bolus
  • the SubQ meal bolus adjustment program 400a includes blocks 410- 428 for processing associated attributes obtained from patient's patient-state information 208a, BG history data 208b, and/or SubQ information 208c.
  • Each block 410-428 may obtain a count and calculate a probability for the associated attributed in a manner of tree-training based on the patient's patient-state information 208a, BG history data 208b, and/or SubQ information 208c obtained from the training data at block 410.
  • Each block 410-428 may include one or more child attribute boxes associated with a parent attribute box. For each attribute , the blocks 410-428 may determine an appropriate box and increment the count (N) by one and calculate a likelihood or probability (P) in each box in accordance with the following equation:
  • a patient's diagnosis attribute obtained from the patient-state information 208a is compared to child boxes associated with the diagnosis attribute.
  • the child box associated with the patient's diagnosis is then selected and the diagnosis count Ndiag is incremented by one and the diagnosis probability Pdiag is calculated using Eq. 7.
  • a patient's age attribute obtained from the patient-state information 208a is compared to child boxes associated with the age attribute.
  • Each child box may correspond to an age in years or an associated range of ages in years.
  • the child box associated with the patient's age is then selected and the age count Nage is incremented by one and the age probability Page is calculated using Eq. 7.
  • a patient's diabetic type DMtype obtained from the patient-state information 208a is compared to child boxes associated with the DMtype attribute.
  • the DMtype attribute at block 416 may include three child boxes: one box for DM1, one box for DM2, and one box for "not recorded.”
  • the child box associated with the patient's DMType is then selected and the DMType count NDMl or NDM2 is incremented by one and the probability PDM1 or PDM2 is calculated using Eq. 7.
  • the patient's body mass index (BMI) is determined by the weight and height of the patient obtained from the patient-state information 208a and compared to child boxes associated with the BMI attribute. Each child box may correspond to a BMI value or an associated range of BMI values. The child box associated with the patient's BMI is then selected and the BMI count NBMI is incremented by one and the BMI probability PBMI is calculated using Eq. 7.
  • the patient' s correction factor (CF) obtained from the SubQ information 208c is compared to child boxes associated with the CF attribute.
  • Each child box may correspond to a CF value or an associated range of CF values.
  • the child box associated with the patient's CF is then selected and the CF count NCF is incremented by one and the CF probability PCF is calculated using Eq. 7.
  • the SubQ meal bolus adjustment program 400a chronologically adjusts the meal bolus insulin dose one dose adjustment at a time.
  • the meal bolus being adjusted is referred to as a Governing Meal Bolus (MealBolusGov).
  • the MealBolusGov may be obtained from the BG history data 208b and compared to child boxes associated with the MealBolusGov attribute. Each child box may correspond to a MealBolusGov value or an associated range of MealBolusGov values.
  • the program 400a selects the child box associated with the patient's MealBolusGov, increments a count Nbolg by one, and calculates the MealBolusGov probability Pbolg using Eq. 7.
  • the program 400a obtains the patient' s next scheduled blood glucose measurement (BGgov) after the patient 10 administers the MealBolusGov from the BG history data 208b and compares the BGgov to child boxes associated with the BGgov attribute. Each child box may correspond to a BGgov value or an associated range of BGgov values.
  • the program 400a selects the child box associated with the patient's BGgov, increments a count NBG by one, and calculates the BGgov probability PBG using Eq. 7.
  • the SubQ meal bolus adjustment program 400a obtains the patient's blood glucose target range BG TR from the SubQ information 208c and compares the BG TR to child boxes associated with the BG TR attribute.
  • the program 400a selects the child box associated with the patient's BG TR , increments a count NTgt by one, and calculates the BG TR probability PTgt using Eq. 7.
  • the SubQ meal bolus adjustment program 400a obtains the patient's insulin-carbohydrate ratio (ICR) from the SubQ information 208c and compares the ICR to child boxes associated with the ICR attribute.
  • the program 400a selects the child box associated with the patient's ICR, increments a count NICR by one, and calculates the ICR probability PICR using Eq. 7.
  • SubQ meal bolus adjustment program 400a processes each of the patient's attributes within blocks 410-428 during the training mode, the program 400a proceeds to block 430 and obtains an Adjusted Meal Bolus (MealBolAdj).
  • MealBolAdj corresponds to the next meal bolus occurring after the
  • MealBolAdj will correspond to the dinner meal bolus occurring on the next day.
  • the program 400a compares the MealBolAdj to child boxes associated with the MealBolAdj attribute. Each child box may correspond to a MealBolAdj value or an associated range of MealBolAdj values.
  • the program 400a selects the child box associated with the patient's MealBolAdj, increments a count NMbolAdj by one, and calculates the MealBolAdj probability PMBolAdj using Eq. 7.
  • the SubQ meal bolus adjustment program 400a may recommend an adjusted insulin dose associated with the MealBolAdj to the patient 10 for the patient 10 to administer.
  • the dosing controller 160 may transmit the MealBolAdj to the patient computing device 110 or to the patient's administration device 123a, 123b.
  • the SubQ meal bolus adjustment program 400a obtains one or more outcome attributes associated with the patient's MealBolAdj .
  • the outcome attribute may include the BGnext that occurs a sufficient period of time (e.g., four to six hours) after the adjusted dose is administered by the patient 10.
  • the program 400a may increment a count for the BGnext ( BGnext) by one. Additionally, during the training mode, the program 400a may calculate an evaluation or a "grade" on the MealBolAdj administered by the patient 10 based on the BGnext.
  • the program 400a may process the BGnext to calculate the BG Percent Error (Err%) using Eq. 5 and increment a count for the Err% (Nerr%) by one.
  • the SubQ meal bolus adjustment program 400a may also calculate a sum for each BGnext (SumBGnext) and each Err% (SumErr%) for each MealBolAdj, and then average the BGnext (MeanBGnext) and the Err% (MeanErr%) for each MealBolAdj .
  • the values for the SumBGnext, the SumErr%, the MeanBGnext, and the MeanErr% may be stored in a child box contained by a parent box corresponding to the MealBolAdj . Accordingly, the meal bolus adjustment program 400a calculates the evaluation or "grade" on each MealBolAdj based on one or more outcome attributes associated with the MealBolAdj .
  • MealBolAdj values yielding favorable outcome attributes are assigned higher "grades" than those resulting in less favorable outcome attributes.
  • the program 400a may identify a best or optimum treatment dose of insulin (e.g., best or optimum MealBolAdj) from one of the MealBolAdj doses of insulin administered by the patient 10 that yields the most favorable outcome attribute.
  • a best or optimum treatment dose of insulin e.g., best or optimum MealBolAdj
  • the SubQ meal bolus adjustment program 400a Upon completing the training mode for each MealBolAdj, the SubQ meal bolus adjustment program 400a stops the iterative process of incrementing the counts and calculating the probabilities within the non-transitory memory 24, 114, 124 at each of blocks 410-430.
  • the training mode of the SubQ meal bolus adjustment program 400a builds a decision tree for a patient population based on the patient-state information 208a, BG history data 208b, and the SubQ information 208c for each patient of the patient population.
  • the treatment mode of the SubQ meal bolus adjustment program 400a may retrieve the decision tree from the non-transitory memory 24, 114, 124 to allow the user 40 (e.g., physical or medical professional) to prescribe an optimum MealBolAdj that is personalized for a patient 10 being treated.
  • the user 40 e.g., physical or medical professional
  • the SubQ meal bolus adjustment program 400a uses the training data contained in the decision tree processed during the training mode for determining an optimum MealBolAdj and recommending the optimum MealBolAdj to a patient 10 in treatment.
  • the treatment mode may commence at block 410 by obtaining patient-state information 208a, BG history data 208b, and SubQ information 208c associated with a new or returning patient 10 being treated.
  • the treatment mode of the SubQ meal bolus adjustment process 400a may assign each of the new or returning patient's attributes into corresponding boxes at each of blocks 410-428. However, the treatment mode does not increment counts or calculate probabilities for the boxes associated with the patient's 10 attributes.
  • the program 400a selects the optimum insulin dose for the MealBolAdj (e.g., optimum MealBolAdj) for the new or returning patient 10 based on the MealBolAdj from the training mode that is associated with a lowest MeanErr%.
  • the treatment mode of the program 400a selects the optimum MealBolAdj that will yield a most favorable outcome attribute (e.g., lowest MeanErr%) based on the training data of the training mode.
  • Block 430 may use the reporting module 80 of the system 100 to recommend the optimum MealBolAdj to the patient 10 being treated through transmission to the patient computing device 110 for the patient 10 to view upon the display, through transmission of an email containing the optimum MealBolAdj, and/or printing the optimum
  • the dosing controller 160 executing the SubQ meal bolus adjustment program 400a at block 430 may transmit the optimum MealBolAdj to the administration device 123 (e.g., pump 123a or smart pen 123b) so that the administration computing device 112d, 112e may instruct the doser 223a, 223b to administer the optimum insulin dose to the patient 10.
  • the administration device 123 e.g., pump 123a or smart pen 123b
  • a SubQ basal adjustment program 400b may operate in a training mode associated with the training program 300a (FIG. 3A) and a treatment mode associated with the treatment program 300b (FIG. 3B).
  • the training mode may commence after the training mode of the SubQ meal bolus adjustment program 400a (FIG. 4A) completes at block 434 of FIG. 4A.
  • the SubQ basal adjustment program 400b uses the new training data (e.g., patient-state information 208a, BG history data 208b, and SubQ information 208c) from block 410 of the SubQ meal bolus adjustment program 400a which is sorted by patient ID and chronologically one basal dose adjustment at a time.
  • each basal insulin dose adjustment and the associated one or more outcome attributes may include a respective date-stamped event.
  • the training mode of the SubQ basal adjustment program 400b includes blocks 442-456 for processing associated attributes obtained from patient's patient-state information 208a, BG history data 208b, and/or SubQ information 208c. Similar to the training mode of the SubQ meal bolus adjustment program 400a (FIG. 4A), each block 442-456 of the SubQ basal adjustment program 400b during the training mode may obtain a count and calculate a probability for the associated attributed in a manner of tree- training based on the patient's patient-state information 208a, BG history data 208b, and/or SubQ information 208c obtained from the training data at block 410. Each block 442-456 may include one or more child attribute boxes associated with a parent attribute box. For each attribute, the blocks 442-456 may determine an appropriate box and increment the count (N) by one and calculate a likelihood or probability (P) in each box using Eq. 7.
  • the training mode of the SubQ basal adjustment program 400b selects the child box, increments the count N by one and calculates the probability P within each box for each of the attributes associated with the patient's diagnosis, age, diabetic type DMtype, and body mass index (BMI) in the same manner as discussed above with reference to blocks 412-418 of the SubQ meal bolus adjustment program 400a of FIG. 4A.
  • the training mode of the SubQ basal adjustment program 400b obtains the patient's blood glucose target range BG TR from the SubQ information 208c and compares the patient's BG TR to child boxes associated with the BG TR attribute at block 450. Similar to block 426 of the SubQ meal bolus adjustment program 400a (FIG. 4A), the training mode of the SubQ basal adjustment program 400b selects the child box associated with the patient's BG TR , increments the BG TR count NTgr by one, and calculates the BG TR probability PTgr using Eq. 7.
  • the training mode of the SubQ basal adjustment program 400b chronologically adjusts the basal insulin dose one dose adjustment at a time.
  • the basal dose being adjusted is referred to as a Governing Basal dose (BasalGov).
  • the program 400b may obtain the patient's BasalGov from the BG history data 208b and compare the patient's BasalGov to child boxes associated with the BasalGov attribute. Each child box may correspond to a BasalGov value or an associated range of BasalGov values.
  • the program 400b selects the child box associated with the patient's BasalGov, increments a count BasalG, and calculates a BasalGov probability PBasalG using Eq. 7.
  • the box associated with the patient's BasalGov is selected by iteratively comparing the patient's BasalGov to an upper bound of the BasalGov attribute
  • the training mode of the SubQ basal adjustment program 400b obtains the patient's next scheduled blood glucose measurement (BGgov) after the patient administers the BasalGov from the BG history data 208b and compares the BGgov to child boxes associated with the BGgov attribute.
  • the BGgov associated with the patient's BasalGov occurs a sufficient amount of time after the patient administers the BasalGov when the dose is at a full activity level when the effects of food and rapid- acting insulin are both absent, and the blood glucose level is stable.
  • the program may flag the BGgov for identification.
  • Each child box may correspond to a BGgov value or an associated range of BGgov values.
  • the program 400a selects the child box associated with the patient's BGgov, increments a count NBG by one, and calculates the BGgov probability PBG using Eq. 7.
  • the training mode of the SubQ basal adjustment program 400b obtains the patient's basal adjustment factor BAF from the SubQ information 208c and compares the patient's BAF to child boxes associated with the BAF attribute at block 456. Each child box may correspond to a BAF value or an associated range of BF values.
  • the program 400b selects the child box associated with the patient's BAF, increments a count BAF by one, and calculates the BAF probability PBAF using Eq. 7.
  • the program 400b proceeds to block 458 and obtains an Adjusted Basal dose (BasalAdj).
  • BasalAdj corresponds to the next scheduled basal dose occurring after the Basal Gov of block 452.
  • the program 400b compares the BasalAdj to child boxes associated with the BasalAdj attribute. Each child box may correspond to a BasalAdj value or an associated range of BasalAdj values.
  • the program 400b selects the child box associated with the patient's BasalAdj, increments a count NBasalAdj by one, and calculates the BasalAdj probability PBasalAdj using Eq. 7.
  • the box associated with the patient's BasalAdj is selected by iteratively comparing the patient's BasalAdj to an upper bound of the BasalAdj attribute (BasalAdj Top) starting from the lowest BasalAdj, and using a logic phrase in accordance with the following expression: IF BasalAdj ⁇ BasalAdj Top THEN
  • the SubQ basal adjustment program 400b may recommend an adjusted insulin dose associated with the BasalAdj to the patient 10 for the patient 10 to administer.
  • the dosing controller 160 may transmit the BasalAdj to the patient computing device 110 or to the patient's administration device 123a, 123b.
  • the SubQ basal adjustment program 400b obtains one or more outcome attributes associated with the patient' s BasalAdj .
  • the outcome attribute may include next scheduled breakfast BG measurement (BGbreakfastNext ) occurring after the adjusted basal dose is administered by the patient at block 358.
  • the program 400b may increment a count for the BGbreakfastNext (NBGbrkNext) by one. Additionally, during the training mode, the program 400b may calculate an evaluation or a "grade" on the
  • BasalAdj administered by the patient 10 based on the BGbreakfastNext may process the BGbreakfastNext to calculate the BGbreakfast Percent Error (BrkErr%) using Eq. 6 and increment a count for the BrkErr% ( BrkErr%) by one.
  • the SubQ basal adjustment program 400b may also calculate a running sum for each BGbreakfastNext (SumBGbrkNext) and each BrkErr% (SumBrkErr%) for each BGbreakfastNext (SumBGbrkNext) and each BrkErr% (SumBrkErr%) for each BGbreakfastNext (SumBGbrkNext) and each BrkErr% (SumBrkErr%) for each BGbreakfastNext (SumBGbrkNext) and each BrkErr% (SumBrkErr%) for each BGbreakfastNext (SumBGbrkNext) and each BrkErr% (SumBrkErr%) for each
  • BasalAdj BasalAdj, and then average the BrkErr% (MeanBrkErr%) for each BasalAdj .
  • the values for the SumBGbrkNext, the SumBrkErr%, and the MeanBrkErr% may be stored in a child box contained by a parent box corresponding to the BasalAdj .
  • the basal adjustment program 400b calculates the evaluation or "grade" on each BasalAdj based on one or more outcome attributes associated with the BasalAdj .
  • BasalAdj values yielding favorable outcome attributes are assigned higher "grades" than those resulting in less favorable outcome attributes.
  • the program 400b may identify a best or optimum treatment dose of insulin (e.g., best or optimum BasalAdj) from one of the BasalAdj doses of insulin administered by the patient 10 that yields the most favorable outcome attribute.
  • the training mode of the SubQ basal adjustment program 400b may proceed back to block 410 and obtain the BG history data 208b for another basal dose-adjustment history.
  • the SubQ basal adjustment program 400b stops the iterative process of incrementing the counts and calculating the probabilities within the non-transitory memory 24, 114, 124 at each of blocks 442-460.
  • the training mode of the SubQ basal adjustment program 400b builds a decision tree for a patient population based on the patient-state information 208a, BG history data 208b, and the SubQ information 208c for each patient of the patient population.
  • the treatment mode of the SubQ basal adjustment program 400b may retrieve the decision tree from the non-transitory memory 24, 114, 124 to allow the user 40 (e.g., physical or medical professional) to prescribe an optimum BasalAdj that is personalized for a patient 10 being treated.
  • the user 40 e.g., physical or medical professional
  • the SubQ basal adjustment program 400b uses the training data contained in the decision tree processed during the training mode for determining an optimum BasalAdj and recommending the optimum BasalAdj to a patient 10 in treatment.
  • the treatment mode may commence at block 410 by obtaining patient- state information 208a, BG history data 208b, and SubQ information 208c associated with a new or returning patient 10 being treated.
  • the treatment mode of the SubQ basal adjustment program 400b may assign each of the new or returning patient's attributes into corresponding boxes at each of blocks 410-456.
  • the treatment mode does not increment counts or calculate probabilities for the boxes associated with the patient's 10 attributes.
  • the program 400a selects the optimum insulin dose for the BasalAdj (e.g., optimum BasalAdj) for the new or returning patient 10 based on the BasalAdj from the training data of the training mode that is associated with a lowest MeanBrkErr%.
  • the treatment mode of the program 400b selects the optimum BasalAdj that will yield a most favorable outcome attribute (e.g., lowest MeanBrkErr%) based on the training data of the training mode.
  • Block 458 may use the reporting module 80 of the system 100 to recommend the optimum BasalAdj to the patient 10 being treated through transmission to the patient computing device 110 for the patient 10 to view upon the display, through transmission of an email containing the optimum BasalAdj, and/or printing the optimum BasalAdj in a report for the patient 10.
  • the dosing controller 160 executing the SubQ basal adjustment program 400b at block 458 may transmit the optimum BasalAdj to the administration device 123 (e.g., pump 123a or smart pen 123b) so that the administration computing device 112d, 112e may instruct the doser 223a, 223b to administer the optimum insulin dose to the patient 10.
  • the administration device 123 e.g., pump 123a or smart pen 123b
  • the ADM adjustment program 400c may operate in training and treatment modes associated with the anti- diabetes program 300c (FIG. 3C). Accordingly, FIG. 4C provides details of the ADM adjustment process associated with the anti-diabetes program 300c (FIG. 3C).
  • the ADM adjustment program 400c may use the new training data (e.g., patient-state information 208a and the BG history data 208b) from block 360 of the ADM program 300c of FIG. 3C which is sorted by patient ID and chronologically one ADM dose-combination adjustment at a time.
  • the training mode of the ADM adjustment program 400c completes the process of obtaining the BG history data 208b associated with ADM dose-combination adjustments and outcome attributes associated therewith.
  • each ADM dose-adjustment adjustment and the associated one or more outcome attributes may include a respective date-stamped event.
  • the ADM adjustment program 400c includes blocks 472-480 for processing associated attributes obtained from the new patient training data from block 322 that includes the patient's patient-state information 208a and the BG history data 208b.
  • Each block 472-480 may obtain a count and calculate a probability for the associated attribute in the manner of tree-training based on the patient's patient-state information 208a and the BG history data 208b.
  • Each block 472-480 may include one or more child attribute boxes associated with a parent attribute box. For each attribute, the blocks 472-480 may determine an appropriate box and increment the count (N) by one and calculate a likelihood or probability (P) in each box using Eq. 7.
  • the BG history data 208b may include a calculation of one or more ADM dose-combinations administered by the patient 10 and the outcome data corresponding to an Ale level associated with the one or more ADM dose-combinations administered by the patient 10. Additionally or alternatively, the outcome data may include a next scheduled blood glucose measurement BGnext occurring a sufficient time after the patient 10 administers the ADM dose-combination, and thereby showing the glycemic result of the ADM dose-combination.
  • the ADM dose-combination refers to a dosing therapy that includes a combination of one or more non-insulin doses of anti-diabetes medications.
  • the training mode of the ADM adjustment program 400c selects the child box, increments the count N by one and calculates the probability P within each box for each of the attributes associated with the patient's diagnosis, age, diabetic type DMtype, and body mass index (BMI) in the same manner as discussed above with reference to blocks 412-418 of the SubQ meal bolus adjustment program 400a (FIG. 4A) and blocks 442-448 of the SubQ basal adjustment program 400b (FIG. 4B).
  • BMI body mass index
  • the training mode of the ADM adjustment program 400c chronologically adjusts the ADM dose-combination one dose adjustment at a time.
  • the program uses the patient's Ale level (AlcGov) to govern the adjustment of the ADM dose-combination.
  • the program 400c may obtain the patient's AlcGov from the BG history data 208b and compare the patient's AlcGov to child boxes associated with the AlcGov attribute. Each child box may correspond to an AlcGov value or an associated range of AlcGov values.
  • the program 400c selects the child box associated with the patient's AlcGov, increments a count NAlcGov, and calculates a AlcGov probability PAlcGov using Eq. 7.
  • block 480 may include child boxes corresponding to AlcTop values equal to 6.5, 7.5, 9.0, 10.0, 11.0, and 20.0.
  • the patient's total daily dose (TDD) of insulin is sorted into boxes. While TDD is a SubQ parameter, the patient's TDD may be obtained from the patient-state information 208a using anyone of Eqs. 4A-4C.
  • the program 400c may compare the patient's TDD to child boxes associated with the TDD attribute. Each child box may correspond to a TDD value or an associated range of TDD values.
  • the program 400c selects the child box associated with the patient's TDD, increments a count NTDD, and calculates a TDD probability PTDD using Eq. 7.
  • ADM adjustment program 400c processes each of the patient's attributes within blocks 472-482, the program 400c proceeds to one of blocks 484, 484a- c and obtains an adjusted ADM dose-combination.
  • Many anti-diabetes medications (ADMs) currently exist and the number of available ADMs for treating patient's continues to increase.
  • ADMs are withdrawn or fall out of favor for prescribing to patients when medical research discovers faults in those ADMs.
  • the ADMs associated with the ADM dose-combinations of blocks 484a-c may be updated with new ADMs and/or with previously used ADMs being withdrawn.
  • the ADM dose-combinations associated with blocks 484a-c may include four different ADMs (e.g., ADM1, ADM2, ADM3, ADM4) that may be used alone or in combination with one or more of the other ADMs.
  • the ADM adjustment program 400c obtains each of the patient's one or more ADMs from the patient-state information 208a and compare the patient's ADM(s) to child boxes associated with the ADM attribute. Each child box may correspond to a specific one of the ADMs.
  • the program 400c selects each child box associated with the patient's one or more ADMs, increments a count NADMl-4 for each box selected, and calculates ADM probability PADMl-4 for each box selected using Eq. 7.
  • the ADM dose-combinations of block 484a are each associated with a monotherapy including an adjusted ADM dose of only one of ADMl, ADM2, ADM3, or ADM4.
  • the ADM dose-combinations of block 484b are associated with a dual -therapy including an adjusted ADM dose-combination of any two of the ADMs 1-4.
  • the ADM dose-combinations of block 484c are associated with a triple-therapy including an adjusted ADM dose-combination of any three of the ADMs 1-4.
  • Therapies including ADM dose-combinations of four or more ADM may exist when more than five ADMs are available for treating patients for delaying, and in some instances, eliminating the progression of diabetes in patients.
  • the training mode of the ADM adjustment program 400c may also increment counts and calculated probabilities for each of the dose- combinations of block 484b associated with the dual -therapy and each of the dose- combinations of block 484c associated with the triple-therapy.
  • the ADM adjustment program 400c obtains one or more outcome attributes associated with each of the adjusted ADM dose-combinations of blocks 484a-484c.
  • an outcome attribute associated with the ADM dose-combinations may include a next Ale result (AlcNext) occurring after the associated adjusted ADM dose-combination is administered by the patient 10.
  • Block 486 may obtain the patient's AlcNext from the BG History data 208b and compare the patient's AlcNext to child boxes associated with the AlcNext attribute. The program 400c selects the child box associated with the patient's AlcNext and increments a count NAlcNext for the selected box by one.
  • the training mode of the ADM adjustment program 400c may proceed back to block 322 and obtain the BG history data 208b for another ADM dose-adjustment history.
  • the ADM adjustment program 400c stops the iterative process of incrementing the counts and calculating the probabilities within the non-transitory memory 24, 114, 124.
  • the ADM adjustment program 400c builds a decision tree for a patient population based on the patient-state information 208a and the BG history data 208b for each patient of the patient population.
  • block 486 averages all of the obtained AlcNext values (MeanAlCNext) for each of the ADM dose- combinations administered by patients of the patient population.
  • the treatment mode of the ADM adjustment program 400c may retrieve the decision tree from the non-transitory memory 24, 114, 124 to allow the user 40 (e.g., physical or medical professional) to prescribe an optimum ADM dose-combination that is personalized for a patient 10 being treated.
  • the treatment mode of the ADM adjustment program 400c may select an optimum ADM dose-combination from one of blocks 484a-484c for a new or returning patient that corresponds to the ADM dose- combination associated with the lowest MeanAlcNext obtained from the training data of the training mode at block 486.
  • a method 500 of determining a treatment dose 336 of insulin for a treated patient 10 includes obtaining 502 training data 310 for a plurality of patients 10 of a patient population at data processing hardware 112, 132, 142, 192 from memory hardware 24, 114, 134, 144, 194 in communication with the data processing hardware 112, 132, 142, 192.
  • the training data 310 includes training blood glucose history data 208b and training patient-state information 208a for each patient 10 of the patient population.
  • the training blood glucose history data 208b includes treatment doses 316, 336 of insulin administered by the patients 10 of the patient population and one or more outcome attributes 318, 338 associated with each treatment dose 316 of insulin.
  • the method 500 includes the data processing hardware 112, 132, 142, 192 identifying 504, for each patient 10 of the patient population, one or more optimum treatment doses 340 of insulin from the treatment doses 316, 336 of insulin yielding favorable outcome attributes 318, 338.
  • the method 500 also includes receiving 506 patient-state information 208a for the treated patient 10 at the data processing hardware 112, 132, 142, 192 and the data processing hardware 112, 132, 142, 192 determining 508 a next recommended treatment dose of insulin (336) for the treated patient 10 based on one or more of the identified optimum treatment doses 340 associated with patients 10 of the patient population having training patient-state information 310, 208a similar to the patient-state information 208a for the treated patient 10.
  • the method 500 includes transmitting 510 the next
  • the portable device 110, 123, 124 may display the next recommended treatment dose.
  • Various implementations of the systems and techniques described here can be realized in digital electronic and/or optical circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or
  • a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
  • a programmable processor which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
  • Implementations of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them.
  • subject matter described in this specification can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a computer readable medium for execution by, or to control the operation of, data processing apparatus.
  • the computer readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter effecting a machine-readable propagated signal, or a combination of one or more of them.
  • data processing apparatus encompass all apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers.
  • the apparatus can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them.
  • a propagated signal is an artificially generated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode information for transmission to suitable receiver apparatus.
  • a computer program (also known as an application, program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.
  • a computer program does not necessarily correspond to a file in a file system.
  • a program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code).
  • a computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.
  • the processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output.
  • the processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).
  • processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer.
  • a processor will receive instructions and data from a read only memory or a random access memory or both.
  • the essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data.
  • a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks.
  • mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks.
  • a computer need not have such devices.
  • a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio player, a Global Positioning System (GPS) receiver, to name just a few.
  • Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks.
  • the processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
  • one or more aspects of the disclosure can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube), LCD (liquid crystal display) monitor, or touch screen for displaying information to the user and optionally a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer.
  • a display device e.g., a CRT (cathode ray tube), LCD (liquid crystal display) monitor, or touch screen for displaying information to the user and optionally a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer.
  • Other kinds of devices can be used to provide interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input
  • a computer can interact with a user by sending documents to and receiving documents from a device that is used by the user; for example, by sending web pages to a web browser on a user's client device in response to requests received from the web browser.
  • a backend component e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a frontend
  • the components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network.
  • communication networks include a local area network ("LAN”) and a wide area network (“WAN”), an inter-network (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks).
  • the computing system can include clients and servers.
  • a client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
  • a server transmits data (e.g., an HTML page) to a client device (e.g., for purposes of displaying data to and receiving user input from a user interacting with the client device).
  • client device e.g., for purposes of displaying data to and receiving user input from a user interacting with the client device.
  • Data generated at the client device e.g., a result of the user interaction

Abstract

La présente invention concerne un procédé (500) qui consiste à obtenir des données d'apprentissage (310) pour une population qui comprennent des données d'historique de glycémie d'apprentissage (208b) comprenant des doses de traitement (316, 336) d'insuline administrée par des patients (10) de la population et un ou plusieurs attributs de résultat (318, 338). Le procédé consiste également à identifier une ou plusieurs doses de traitement optimales (340) d'insuline provenant des doses de traitement donnant des attributs de résultat favorables. Le procédé comprend également la réception d'informations d'état de patient pour un patient traité, la détermination d'une dose de traitement recommandée suivante (336) d'insuline pour le patient traité sur la base d'une ou de plusieurs des doses de traitement optimales identifiées associées aux patients de la population ayant des informations d'état de patient d'apprentissage similaires aux informations d'état de patient pour le patient traité, et la transmission de la dose de traitement recommandée suivante à un dispositif portable (110, 123, 124).
PCT/US2016/047806 2015-08-20 2016-08-19 Conseiller de thérapie pour la gestion du diabète WO2017031440A1 (fr)

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JP2018509740A JP6858751B2 (ja) 2015-08-20 2016-08-19 糖尿病管理療法アドバイザ
AU2016308953A AU2016308953B2 (en) 2015-08-20 2016-08-19 Diabetes management therapy advisor
EP16837919.6A EP3337402A4 (fr) 2015-08-20 2016-08-19 Conseiller de thérapie pour la gestion du diabète
CA2993275A CA2993275C (fr) 2015-08-20 2016-08-19 Conseiller de therapie pour la gestion du diabete
IL256999A IL256999B (en) 2015-08-20 2018-01-18 Diabetes care management consultant

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US62/207,613 2015-08-20

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10046113B2 (en) 2011-12-21 2018-08-14 Monarch Medical Technologies, Llc Systems and methods for determining insulin therapy for a patient
EP3642789A4 (fr) * 2017-06-20 2021-01-27 Chan, Sidney Soong-Ling Procédé et système de surveillance d'un plan de traitement du diabète à

Families Citing this family (33)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2952167C (fr) 2014-06-13 2023-06-13 Aterica Inc. Systeme et dispositif de gestion de dispositifs d'administration de medicaments
US10085640B2 (en) * 2015-05-12 2018-10-02 Dexcom, Inc. Distributed system architecture for continuous glucose monitoring
WO2018132315A1 (fr) * 2017-01-11 2018-07-19 Abbott Diabetes Care Inc. Systèmes, dispositifs et procédés pour des calculs expérimentaux de dosage de médicament
US20180272066A1 (en) 2017-03-24 2018-09-27 Medtronic Minimed, Inc. Patient management systems and adherence recommendation methods
CN110582231B (zh) 2017-05-05 2023-05-16 伊莱利利公司 生理葡萄糖的闭环控制
US20210193285A1 (en) * 2017-10-19 2021-06-24 Dreamed Diabetes Ltd. A system and method for use in disease treatment management
US11676734B2 (en) * 2017-11-15 2023-06-13 Medtronic Minimed, Inc. Patient therapy management system that leverages aggregated patient population data
EP3724891A1 (fr) 2017-12-12 2020-10-21 Bigfoot Biomedical, Inc. Systèmes, dispositifs et méthodes d'injection de médicament et de gestion de la maladie
US11464459B2 (en) * 2017-12-12 2022-10-11 Bigfoot Biomedical, Inc. User interface for diabetes management systems including flash glucose monitor
US11116899B2 (en) 2017-12-12 2021-09-14 Bigfoot Biomedical, Inc. User interface for diabetes management systems and devices
US11083852B2 (en) 2017-12-12 2021-08-10 Bigfoot Biomedical, Inc. Insulin injection assistance systems, methods, and devices
US10987464B2 (en) 2017-12-12 2021-04-27 Bigfoot Biomedical, Inc. Pen cap for insulin injection pens and associated methods and systems
US11077243B2 (en) 2017-12-12 2021-08-03 Bigfoot Biomedical, Inc. Devices, systems, and methods for estimating active medication from injections
US11197964B2 (en) 2017-12-12 2021-12-14 Bigfoot Biomedical, Inc. Pen cap for medication injection pen having temperature sensor
US11568974B2 (en) 2017-12-21 2023-01-31 Aseko, Inc. Advising diabetes medications
CN111542884B (zh) 2017-12-21 2024-03-15 益首药物治疗股份公司 生理葡萄糖的闭环控制
FR3083075B1 (fr) * 2018-06-28 2023-01-13 Mirambeauappcare Procede de gestion ou d’assistance au controle d’un parametre physiologique d’un individu, en particulier du taux de glycemie
MX2021000798A (es) * 2018-07-26 2021-04-12 Lilly Co Eli Sistemas y metodos para la receta remota de regimenes de dosificacion de medicamentos.
WO2020043734A1 (fr) 2018-08-28 2020-03-05 Novo Nordisk A/S Systèmes et procédés permettant de fournir des recommandations sur la dose de médicaments contenus dans un récipient destinés au traitement du diabète
WO2020043922A1 (fr) 2018-08-31 2020-03-05 Novo Nordisk A/S Prédiction de dose d'insuline fondée sur un horion rétrospectif
US11416567B2 (en) 2018-09-20 2022-08-16 Optum, Inc. Method and system for individualized presentation of prioritized information
EP3731233A1 (fr) * 2019-04-24 2020-10-28 Digital Diabetes Analytics Sweden AB Système d'aide à la décision et procédé associé
AU2020314831A1 (en) * 2019-07-16 2022-02-24 Beta Bionics, Inc. Blood glucose control system
CN112463973A (zh) * 2019-09-06 2021-03-09 医渡云（北京）技术有限公司 医学知识图谱的构建方法、装置、介质及电子设备
JP2023503792A (ja) * 2019-10-04 2023-02-01 ベータ バイオニクス，インコーポレイテッド 血糖制御システム
DE102020001563A1 (de) * 2020-03-10 2021-09-16 Drägerwerk AG & Co. KGaA Medizinsystem zum Bereitstellen einer Behandlungsempfehlung
KR102521085B1 (ko) * 2020-10-30 2023-04-12 이오플로우(주) 인슐린 패치를 포함하는 인공췌장 제어 방법 및 그 장치
CN112354042A (zh) * 2020-12-01 2021-02-12 南通市肿瘤医院 一种镇痛泵流量控制方法和装置
CN113270204A (zh) * 2021-06-04 2021-08-17 荣曦 一种胰岛素泵初始剂量的预测方法
CN113628755B (zh) * 2021-08-20 2024-03-12 东南大学附属中大医院 患者的血糖控制方法、装置、设备以及存储介质
US20230095963A1 (en) * 2021-09-27 2023-03-30 Medtronic Minimed, Inc. Medicine administration and tracking systems and methods including customized user alerts
WO2023173129A1 (fr) * 2022-03-11 2023-09-14 Aseko, Inc. Conseils de traitement du diabète
CN115445022B (zh) * 2022-09-30 2023-08-18 中南大学湘雅二医院 一种智能化的胰岛素泵控制系统

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6024699A (en) * 1998-03-13 2000-02-15 Healthware Corporation Systems, methods and computer program products for monitoring, diagnosing and treating medical conditions of remotely located patients
US20070179434A1 (en) * 2005-12-08 2007-08-02 Stefan Weinert System and method for determining drug administration information
US20090006061A1 (en) * 2007-06-27 2009-01-01 Roche Diagnostics Operations, Inc. System for developing patient specific therapies based on dynamic modeling of patient physiology and method thereof
US20110251472A1 (en) * 2006-06-16 2011-10-13 Stefan Weinert System for collecting patient information for diabetes management
US20150217054A1 (en) * 2014-01-31 2015-08-06 Aseko, Inc. Insulin Management

Family Cites Families (385)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US561422A (en) 1896-06-02 Paul minnis
US4055175A (en) 1976-05-07 1977-10-25 Miles Laboratories, Inc. Blood glucose control apparatus
FR2387659A1 (fr) 1977-04-21 1978-11-17 Armines Dispositif de controle et regulation de la glycemie
US4151845A (en) 1977-11-25 1979-05-01 Miles Laboratories, Inc. Blood glucose control apparatus
US4403984A (en) 1979-12-28 1983-09-13 Biotek, Inc. System for demand-based adminstration of insulin
US4464170A (en) 1982-09-29 1984-08-07 Miles Laboratories, Inc. Blood glucose control apparatus and method
US4731726A (en) 1986-05-19 1988-03-15 Healthware Corporation Patient-operated glucose monitor and diabetes management system
US4981779A (en) 1986-06-26 1991-01-01 Becton, Dickinson And Company Apparatus for monitoring glucose
JPH0778480B2 (ja) * 1987-04-16 1995-08-23 日機装株式会社 溶液中の溶質濃度測定電極の自動校正方法
US5216597A (en) 1987-05-01 1993-06-01 Diva Medical Systems Bv Diabetes therapy management system, apparatus and method
US4850959A (en) 1988-08-02 1989-07-25 Bioresearch, Inc. Bioelectrochemical modulation of biological functions using resonant/non-resonant fields synergistically
US4947845A (en) 1989-01-13 1990-08-14 Pacesetter Infusion, Ltd. Method of maximizing catheter longevity in an implantable medication infusion system
US4911168A (en) 1989-01-13 1990-03-27 Pacesetter Infusion, Ltd. Method of screening and selecting intraperitoneal medication infusion pump candidates
US5101814A (en) 1989-08-11 1992-04-07 Palti Yoram Prof System for monitoring and controlling blood glucose
US5091190A (en) 1989-09-05 1992-02-25 Alza Corporation Delivery system for administration blood-glucose lowering drug
US5251126A (en) 1990-10-29 1993-10-05 Miles Inc. Diabetes data analysis and interpretation method
GB9024771D0 (en) 1990-11-14 1991-01-02 Axis Research Assay
GB9104037D0 (en) 1991-02-26 1991-04-10 Hospital For Sick The Children Process for the phosphorylation of insulin
SE9101381D0 (sv) 1991-05-07 1991-05-07 Tomas Moks Peptide hormone solution
US7624028B1 (en) 1992-11-17 2009-11-24 Health Hero Network, Inc. Remote health monitoring and maintenance system
US5956501A (en) 1997-01-10 1999-09-21 Health Hero Network, Inc. Disease simulation system and method
US6186145B1 (en) 1994-05-23 2001-02-13 Health Hero Network, Inc. Method for diagnosis and treatment of psychological and emotional conditions using a microprocessor-based virtual reality simulator
JPH08500123A (ja) 1993-01-25 1996-01-09 ザ ベス イスラエル ホスピタル アソシエイション Ｉｇｆ−ｉ感受性細胞のバリヤー特性の修飾方法，診断方法及びスクリーニング方法
US5917052A (en) 1994-09-28 1999-06-29 Shaman Pharmaceuticals, Inc. Hypoglycemic agent from cryptolepis
MY115050A (en) 1995-10-16 2003-03-31 Mead Johnson Nutrition Co Diabetic nutritional product having controlled absorption of carbohydrate
US5821217A (en) 1995-10-27 1998-10-13 Beth Israel Deaconess Medical Center, Inc. Enteral formulation: low in fat and containing protein hydrolysates
JP2000500656A (ja) 1995-11-22 2000-01-25 ミニメッド インコーポレイティド 化学的増幅及び光学センサーを用いる生物分子の検出
AU718254B2 (en) 1996-01-19 2000-04-13 Betagene, Inc. Recombinant expression of proteins from secretory cell lines
US8734339B2 (en) 1996-12-16 2014-05-27 Ip Holdings, Inc. Electronic skin patch for real time monitoring of cardiac activity and personal health management
US6391312B1 (en) 1997-01-23 2002-05-21 Sumitomo Pharmaceuticals Co., Limited Remedies for diabetes
US20010002269A1 (en) 1997-05-06 2001-05-31 Zhao Iris Ginron Multi-phase food & beverage
US6558351B1 (en) 1999-06-03 2003-05-06 Medtronic Minimed, Inc. Closed loop system for controlling insulin infusion
US7267665B2 (en) 1999-06-03 2007-09-11 Medtronic Minimed, Inc. Closed loop system for controlling insulin infusion
US5929055A (en) 1997-06-23 1999-07-27 The Research Foundation Of State University Of New York Therapeutic method for management of diabetes mellitus
EP1030557A4 (fr) 1997-10-02 2002-07-31 Univ California Administration de proteines par l'expression des glandes secretoires
CO4970787A1 (es) 1997-12-23 2000-11-07 Lilly Co Eli Composiciones insolubles de insulina y derivados de insulina que controlan la glucosa sanguinea
EP1051141A4 (fr) 1998-01-09 2001-05-23 Amylin Pharmaceuticals Inc Compositions pharmaceutiques a agoniste d'amyline, contenant de l'insuline
WO1999044496A1 (fr) 1998-03-05 1999-09-10 Universal Healthwatch, Inc. Appareil de mesure non invasive du glucose ou d'autres constituants dans l'humeur aqueuse par spectroscopie a infrarouge
JP2002510623A (ja) 1998-04-02 2002-04-09 メルク エンド カムパニー インコーポレーテッド 抗糖尿病薬
EP1087753A4 (fr) 1998-05-19 2004-06-30 Sdg Inc Systeme de liberation ciblee de medicament encapsule dans des liposomes
US6537806B1 (en) 1998-06-02 2003-03-25 University Of Washington Compositions and methods for treating diabetes
CN1324234A (zh) 1998-09-25 2001-11-28 格利科克斯有限公司 果糖胺氧化酶,拮抗剂和抑制剂
US6615081B1 (en) 1998-10-26 2003-09-02 Birinder R. Boveja Apparatus and method for adjunct (add-on) treatment of diabetes by neuromodulation with an external stimulator
US6540672B1 (en) 1998-12-09 2003-04-01 Novo Nordisk A/S Medical system and a method of controlling the system for use by a patient for medical self treatment
DK1144028T3 (da) 1998-11-30 2004-10-18 Novo Nordisk As System til at hjælpe en bruger under medicinsk selvbehandling, hvor nævnte selvbehandling omfatter en flerhed af handlinger
US20070293742A1 (en) 1998-11-30 2007-12-20 Novo Nordisk A/S Medical System And A Method Of Controlling The System For Use By A Patient For Medical Self Treatment
US8700161B2 (en) 1999-03-05 2014-04-15 Metacure Limited Blood glucose level control
JP2002543092A (ja) 1999-04-27 2002-12-17 イーライ・リリー・アンド・カンパニー 肺投与用インスリン結晶
US6669663B1 (en) 1999-04-30 2003-12-30 Medtronic, Inc. Closed loop medicament pump
US6890568B2 (en) 1999-05-24 2005-05-10 Grant Pierce Method for management of blood glucose levels
US7806886B2 (en) 1999-06-03 2010-10-05 Medtronic Minimed, Inc. Apparatus and method for controlling insulin infusion with state variable feedback
WO2000074698A1 (fr) 1999-06-04 2000-12-14 Indian Council Of Medical Research Produit therapeutique a base de plantes
AU5453300A (en) 1999-06-29 2001-01-31 Eli Lilly And Company Insulin crystals for pulmonary administration
JP2003509442A (ja) 1999-09-15 2003-03-11 ザ・レジェンツ・オブ・ザ・ユニバーシティー・オブ・カリフォルニア 選択された蛍光特性を有するグルコース感知分子
DE19954233A1 (de) 1999-11-11 2001-05-31 Nutricia Nv Diabetikernahrung
US6658396B1 (en) * 1999-11-29 2003-12-02 Tang Sharon S Neural network drug dosage estimation
US6572542B1 (en) 2000-03-03 2003-06-03 Medtronic, Inc. System and method for monitoring and controlling the glycemic state of a patient
DZ3338A1 (fr) 2000-03-29 2001-10-04 Univ Virginia Méthode, systeme et programme d'ordinateur pour l'évaluation de régulation de glycemique du diabète à partir de données contrôllées automatiquement
JP4580542B2 (ja) 2000-05-17 2010-11-17 株式會社バイオニア 肥満又は糖尿病治療用微生物及びその微生物を含む医薬組成物
CA2409501A1 (fr) 2000-05-25 2001-11-29 Healthetech, Inc. Suivi physiologique a l'aide d'un dispositif se portant au poignet
US6605039B2 (en) 2000-07-24 2003-08-12 Medtronic, Inc. Cell-based biosensors suitable for implantable medical device applications
JP2004505042A (ja) 2000-08-02 2004-02-19 ファーマニュートリエンツ 糖尿病及びグルコース変性の予防及び／または治療のための方法及び組成物
US6365176B1 (en) 2000-08-08 2002-04-02 Functional Foods, Inc. Nutritional supplement for patients with type 2 diabetes mellitus for lipodystrophy
US6572545B2 (en) 2000-09-22 2003-06-03 Knobbe, Lim & Buckingham Method and apparatus for real-time control of physiological parameters
WO2002036139A1 (fr) 2000-11-01 2002-05-10 Biovan, Inc. Régulation de la glycémie
DE60133667T2 (de) 2000-11-30 2009-06-18 Arkray, Inc. Mit kommentareingabefunktion ausgestattete messvorrichtung
US6560471B1 (en) 2001-01-02 2003-05-06 Therasense, Inc. Analyte monitoring device and methods of use
PL359266A1 (en) 2001-02-08 2004-08-23 Inverness Medical Limited A personal condition management system
WO2002066986A2 (fr) 2001-02-15 2002-08-29 Medtronic Minimed, Inc. Polymeres fonctionnalises dotes de boronate fluorescent et procedes de fabrication de ces polymeres
CA2449898C (fr) 2001-06-18 2018-03-06 Neptune Technologies & Bioressources Inc. Krill et/ou extraits marins pour la prevention et/ou le traitement des maladies cardiovasculaires, de l'arthrite, du cancer de la peau, du diabete, du syndrome premenstruel et du transport transdermique
US6544212B2 (en) 2001-07-31 2003-04-08 Roche Diagnostics Corporation Diabetes management system
JP3775263B2 (ja) 2001-08-10 2006-05-17 ニプロ株式会社 記録媒体およびこの記録媒体を用いた血糖測定システム
JP2004538078A (ja) 2001-08-20 2004-12-24 インバネス・メディカル・リミテッド 無線糖尿病管理装置および無線糖尿病管理装置の使用方法
MY135783A (en) 2001-09-07 2008-06-30 Meiji Dairies Corp Nutritional composition for controlling blood sugar level
US6740072B2 (en) 2001-09-07 2004-05-25 Medtronic Minimed, Inc. System and method for providing closed loop infusion formulation delivery
US6827702B2 (en) 2001-09-07 2004-12-07 Medtronic Minimed, Inc. Safety limits for closed-loop infusion pump control
KR20030025200A (ko) 2001-09-19 2003-03-28 (주)바이오니아 아밀라제의 활성을 억제할 수 있는 옥수수 추출물, 이를함유하는 비만증 또는 당뇨병 치료 또는 예방용 의약적조성물 및 식품 첨가물 그리고 그들의 제조방법
AU2002346491A1 (en) 2001-12-19 2003-07-09 Eli Lilly And Company Crystalline compositions for controlling blood glucose
US7204823B2 (en) 2001-12-19 2007-04-17 Medtronic Minimed, Inc. Medication delivery system and monitor
WO2003061643A1 (fr) 2002-01-25 2003-07-31 Laboratorios Silanes, S.A. De C.V. Composition pharmaceutique permettant de surveiller le glucose dans le sang de patients souffrant de diabete de type 2
US20030199445A1 (en) 2002-02-07 2003-10-23 Knudsen Lotte Bjerre Use of GLP-1 compound for treatment of critically ill patients
US20080172026A1 (en) * 2006-10-17 2008-07-17 Blomquist Michael L Insulin pump having a suspension bolus
US7108680B2 (en) 2002-03-06 2006-09-19 Codman & Shurtleff, Inc. Closed-loop drug delivery system
TW200304372A (en) 2002-03-20 2003-10-01 Kanegafuchi Chemical Ind Compositions for diabetes
GB0206792D0 (en) 2002-03-22 2002-05-01 Leuven K U Res & Dev Normoglycemia
WO2003090614A1 (fr) 2002-04-25 2003-11-06 Matsushita Electric Industrial Co., Ltd. Dispositif de determination de dose, injecteur et systeme de gestion sanitaire
BRPI0309689B1 (pt) 2002-04-30 2021-06-29 Unigen, Inc Composições compreendendo uma mistura de flavonóides de anel-b-livre e flavanos e usos das mesmas
US20060264886A9 (en) 2002-05-06 2006-11-23 Pettis Ronald J Method for altering insulin pharmacokinetics
JP2003319758A (ja) 2002-05-07 2003-11-11 Unitika Ltd Ｄ−グルコース吸収抑制方法及びｄ−グルコース吸収抑制剤
US20040018533A1 (en) 2002-06-04 2004-01-29 Adam Gail Isabel Reid Diagnosing predisposition to fat deposition and therapeutic methods for reducing fat deposition and treatment of associated conditions
US20050014158A1 (en) 2002-06-27 2005-01-20 Adam Gail Isabel Reid Therapeutic methods for reducing fat deposition and treating associated conditions
US20050267195A1 (en) 2002-06-28 2005-12-01 Imao Mikoshiba Drug composition for blood sugar control
EP1382363A1 (fr) 2002-07-15 2004-01-21 Novo Nordisk A/S Systéme en boucle fermée de gestion pour contrôler les niveaux de taux de glucose sanguin
FR2842735B1 (fr) 2002-07-26 2006-01-06 Flamel Tech Sa Microcapsules a liberation modifiee de principes actifs peu solubles pour l'administration per os
US20050197553A1 (en) * 2002-07-30 2005-09-08 Colleen Cooper Patient management of diabetes treatment
ES2456068T3 (es) * 2002-08-13 2014-04-21 University Of Virginia Patent Foundation Método, sistema y producto de programa informático para el procesamiento de datos de auto-supervisión de glucemia (SMBG) para mejorar la autogestión diabética
US8571801B2 (en) 2002-08-28 2013-10-29 Atkins Nutritionals, Inc. Methods and systems for determining and controlling glycemic responses
JP2004095048A (ja) 2002-08-30 2004-03-25 Toshiba Corp 不揮発性半導体メモリ
US7404796B2 (en) 2004-03-01 2008-07-29 Becton Dickinson And Company System for determining insulin dose using carbohydrate to insulin ratio and insulin sensitivity factor
WO2004028469A2 (fr) 2002-09-27 2004-04-08 Martek Biosciences Corporation Regulation glycemique amelioree dans le prediabete et/ou le diabete de type ii au moyen de l'acide docosahexaenoique
JP4599296B2 (ja) 2002-10-11 2010-12-15 ベクトン・ディキンソン・アンド・カンパニー 単一針または多針皮内（ｉｄ）送達装置に結合されたフィードバックまたはモデルベースコントローラを使用して患者の体内の物質の濃度の連続長期制御を開始し維持するためのシステムおよび方法
US7137951B2 (en) 2002-10-23 2006-11-21 Joseph Pilarski Method of food and insulin dose management for a diabetic subject
WO2004060387A1 (fr) 2002-12-27 2004-07-22 Diobex, Inc. Compositions et procedes de prevention et reduction de l'hypoglycemie induite par l'insuline
US9872890B2 (en) 2003-03-19 2018-01-23 Paul C. Davidson Determining insulin dosing schedules and carbohydrate-to-insulin ratios in diabetic patients
JP4800928B2 (ja) 2003-03-19 2011-10-26 ヘブルホワイト、ハリー 糖尿病患者のインシュリン投与療法を定期的に調整するための装置及びインシュリン送達装置内又は医師のコンピュータ内の、糖尿病患者のインシュリン投与量パラメータを調整する装置
US7504118B2 (en) 2003-04-11 2009-03-17 Fhg Corporation Dietary supplements containing extracts of cinnamon and methods of using same to enhance creatine transport
KR100527154B1 (ko) 2003-05-23 2005-11-08 최수봉 인터넷을 통한 인슐린펌프 제어방법
JP2005029570A (ja) 2003-06-16 2005-02-03 Tokiwa Shokubutsu Kagaku Kenkyusho:Kk コロソリン酸の製造方法
US20050054818A1 (en) 2003-07-02 2005-03-10 Brader Mark Laurence Crystalline compositions for controlling blood glucose
US7591801B2 (en) 2004-02-26 2009-09-22 Dexcom, Inc. Integrated delivery device for continuous glucose sensor
KR100567837B1 (ko) 2003-10-24 2006-04-05 케이제이헬스케어 주식회사 혈당측정이 가능한 이동통신단말기와 결합되는 인슐린펌프와, 인슐린 펌프의 제어정보 전송을 위한 네트워크시스템
WO2005041022A1 (fr) 2003-10-24 2005-05-06 Judy Singley Procede, systeme et programme informatique permettant de calculer le rapport hydrates de carbone/insuline sur la base du poids d'un aliment
US20050096637A1 (en) 2003-10-31 2005-05-05 Medtronic, Inc. Sensing food intake
US20080119421A1 (en) 2003-10-31 2008-05-22 Jack Tuszynski Process for treating a biological organism
CN100496536C (zh) 2003-11-04 2009-06-10 明治乳业株式会社 α-葡萄糖苷酶活性抑制剂
AU2004286716B2 (en) 2003-11-06 2010-03-25 Lifescan, Inc. Drug delivery pen with event notification means
US8532730B2 (en) 2006-10-04 2013-09-10 Dexcom, Inc. Analyte sensor
EP1711101A1 (fr) 2004-01-15 2006-10-18 Glucon Inc. Glucometre portable
WO2006004574A2 (fr) 2004-02-19 2006-01-12 Abbott Laboratories Methode d'utilisation de gamma cyclodextrine dans la regulation de la glycemie et de la secretion d'insuline
WO2005081173A1 (fr) 2004-02-19 2005-09-01 Edward Henry Mathews Regle a calcul pour la surveillance de la glycemie
WO2005081171A2 (fr) 2004-02-19 2005-09-01 Edward Henry Mathews Calculateur de bol d'insuline pour dispositif de communications mobiles
WO2005081119A2 (fr) 2004-02-19 2005-09-01 Edward Henry Mathews Systeme interactif d'enseignement du diabete
WO2005081170A2 (fr) 2004-02-19 2005-09-01 Edward Henry Mathews Gestion du diabete et systeme de base de donnees de patient via un dispositif de communication mobile et une unite de communication distante
US7651845B2 (en) 2004-05-13 2010-01-26 The Regents Of The University Of California Method and apparatus for glucose control and insulin dosing for diabetics
WO2005110222A1 (fr) 2004-05-18 2005-11-24 Okto Medical Appliance Co., Ltd. Systeme de commande du taux de sucre dans le sang et technique de traitement medical utilisant ce systeme.
CA2573261A1 (fr) 2004-07-09 2006-01-19 Meiji Dairies Corporation Composition servant a accroitre la production de ppar et/ou d'un facteur associe aux ppar
WO2006022629A1 (fr) 2004-07-22 2006-03-02 Sequenom, Inc. Procédés d’identification de risque de diabète de type ii et leurs traitements
WO2006022633A1 (fr) 2004-07-22 2006-03-02 Sequenom, Inc. Methodes d'identification de risque de diabete de type ii et traitements associes
WO2006022638A1 (fr) 2004-07-22 2006-03-02 Sequenom, Inc. Méthodes d’identification du risque d’apparition de diabètes de type ii et traitements associés
CA2574610A1 (fr) 2004-07-22 2006-03-02 Sequenom, Inc. Methodes d'evaluation du risque d'apparition de diabetes de type ii et traitements associes
WO2006022619A2 (fr) 2004-07-22 2006-03-02 Sequenom, Inc. Methodes d'identification des risques de diabete de type ii et son traitement
WO2006022636A1 (fr) 2004-07-22 2006-03-02 Sequenom, Inc. Méthodes d’identification du risque d’apparition de diabètes de type ii et traitements associés
WO2006022634A1 (fr) 2004-07-22 2006-03-02 Sequenom, Inc. Méthodes d’identification du risque d’apparition de diabètes de type ii et traitements associés
US20060040003A1 (en) 2004-08-10 2006-02-23 Alvin Needleman Dietary supplement for supressing appetite, enhancing and extending satiety, improving glycemic control, and stimulant free
US20070060549A1 (en) 2004-08-10 2007-03-15 Friesen Albert D Combination therapies employing ace inhibitors and uses thereof for the treatment of diabetic disorders
US20060046962A1 (en) 2004-08-25 2006-03-02 Aegis Therapeutics Llc Absorption enhancers for drug administration
US20120213886A1 (en) 2004-08-25 2012-08-23 U.S. Dept. Of Veterans Affairs Compositions and Methods to Lower Glycohemoglobin Levels
US7291107B2 (en) 2004-08-26 2007-11-06 Roche Diagnostics Operations, Inc. Insulin bolus recommendation system
EP1698898A3 (fr) 2004-09-21 2006-10-11 Foundation of Biomedical Research of the Academy of Athens Pronostic, diagnostic et signification thérapeutique lors de la détection et de l'utilisation de l'antigène TIN (antigène de la néphrite tubulo-intersticielle) et de ses segments
US20090247931A1 (en) * 2004-09-23 2009-10-01 Novo Nordisk A/S Device for self-care support
US20060078593A1 (en) 2004-09-27 2006-04-13 Strozier Deborah C Nutritional compostions comprising a soluble viscous fiber in a solid crisp matrix
WO2006044556A2 (fr) 2004-10-14 2006-04-27 Galileo Pharmaceuticals, Inc. Methodes de traitement du diabete
US8117020B2 (en) 2004-12-03 2012-02-14 Roche Diagnostics Operations, Inc. Method to determine the degree and stability of blood glucose control in patients with diabetes mellitus via creation and continuous updating of new statistical indicators
EP2481389B1 (fr) * 2004-12-24 2016-05-04 Dolphst Pty Ltd Formulations et traitements pour la trichologie
ITMI20050010A1 (it) 2005-01-05 2006-07-06 Aboca S P A Composizioni farmaceutiche nutraceutiche dietetiche nutrizionali a base di fibre vegetali
CA2491763A1 (fr) 2005-01-06 2006-07-06 Tassos P. Anastassiades Methode de maitrise de la glycemie a l'aide de glucosamines n-acylees
WO2006079124A2 (fr) 2005-01-24 2006-07-27 Edward Henry Mathews Appareil et procede servant a prevoir l'effet de denrees alimentaires ingerees ou d'exercice sur le taux de glycemie d'un patient et a suggerer une action corrective
US20060173260A1 (en) 2005-01-31 2006-08-03 Gmms Ltd System, device and method for diabetes treatment and monitoring
WO2006113978A2 (fr) 2005-04-27 2006-11-02 Jallal Messadek Combinaisons insuliniques
US7390663B2 (en) 2005-02-23 2008-06-24 Streck, Inc. Process, composition and kit for providing a stable whole blood calibrator/control
US20060188995A1 (en) 2005-02-24 2006-08-24 Ryan Wayne L Process, composition and kit for providing a stable whole blood calibrator/control
KR100750988B1 (ko) 2005-03-18 2007-08-22 주식회사 유니젠 천연물유래 진세노사이드를 포함하는 당뇨 또는혈당조절이상의 예방 또는 치료용 약학 조성물
BRPI0500959A (pt) 2005-03-23 2006-11-21 Unicamp uso farmacológico de inibidor da expressão da proteìna coativador 1 alfa do receptor ativado por proliferador do peroxisoma (pgc-1(alfa)) para o tratamento de diabetes mellitus, resistência à insulina e sìndrome metabólica, seu composto e sua composição farmacêuticos
US20090054753A1 (en) 2007-08-21 2009-02-26 Mark Ries Robinson Variable Sampling Interval for Blood Analyte Determinations
US20120065482A1 (en) 2005-04-08 2012-03-15 Mark Ries Robinson Determination of blood pump system performance and sample dilution using a property of fluid being transported
JP4781710B2 (ja) * 2005-05-12 2011-09-28 シスメックス株式会社 治療効果予測システム及びそのプログラム
US20060263450A1 (en) 2005-05-17 2006-11-23 Julius Oben Method and composition for reducing body weight and improving control of body lipids
US7509156B2 (en) 2005-05-18 2009-03-24 Clarian Health Partners, Inc. System for managing glucose levels in patients with diabetes or hyperglycemia
WO2006130901A1 (fr) 2005-06-07 2006-12-14 Bayer Healthcare Ag Lutte contre les anomalies metaboliques
US8251904B2 (en) 2005-06-09 2012-08-28 Roche Diagnostics Operations, Inc. Device and method for insulin dosing
WO2006136423A2 (fr) 2005-06-24 2006-12-28 Dsm Ip Assets B.V. Nouvelle utilisation de composes organiques
EP1921981B1 (fr) 2005-07-29 2012-04-04 Universita' Degli Studi di Udine Appareil et methode destines a prevenir l'hypoglycemie chez un patient humain diabetique de type 1 pendant une activite physique
CA2617226A1 (fr) 2005-08-01 2007-02-08 Mannkind Corporation Methode de conservation de la fonction des cellules produisant l'insuline
US20070078314A1 (en) 2005-09-30 2007-04-05 Grounsell Richard L System and method for measuring and predicting insulin dosing rates
US7704226B2 (en) 2005-11-17 2010-04-27 Medtronic Minimed, Inc. External infusion device with programmable capabilities to time-shift basal insulin and method of using the same
US20070168224A1 (en) 2005-11-22 2007-07-19 Letzt Alan M Advanced diabetes management system (adms)
US8557274B2 (en) 2005-12-06 2013-10-15 Purdue Research Foundation Slowly digesting starch and fermentable fiber
US8057840B2 (en) 2006-01-25 2011-11-15 Tate & Lyle Ingredients Americas Llc Food products comprising a slowly digestible or digestion resistant carbohydrate composition
US7824333B2 (en) 2006-03-31 2010-11-02 Lifescan, Inc. Diabetes management methods and systems
US8346335B2 (en) 2008-03-28 2013-01-01 Abbott Diabetes Care Inc. Analyte sensor calibration management
GB2436873A (en) 2006-04-07 2007-10-10 Univ Cambridge Tech Blood glucose monitoring systems
US20070282186A1 (en) 2006-05-02 2007-12-06 Adrian Gilmore Blood glucose monitor with an integrated data management system
WO2007149533A2 (fr) * 2006-06-19 2007-12-27 Dose Safety Système, procédé et article pour commander la distribution de l'insuline
WO2008005761A2 (fr) 2006-06-30 2008-01-10 Healthy Interactions, Inc. System, procede et dispositif pour procurer des informations de sante
US20090312250A1 (en) 2006-07-14 2009-12-17 Postech Academy-Industry Foundation Epidermal growth factor increasing insulin secretion and lowering blood glucose
JP5299900B2 (ja) 2006-07-28 2013-09-25 国立大学法人金沢大学 糖尿病関連肝臓由来分泌タンパク質の２型糖尿病または血管障害の診断または治療への利用
US9056165B2 (en) 2006-09-06 2015-06-16 Medtronic Minimed, Inc. Intelligent therapy recommendation algorithm and method of using the same
US20080172030A1 (en) 2006-10-17 2008-07-17 Blomquist Michael L Insulin pump having aweekly schedule
US8204729B2 (en) 2006-11-01 2012-06-19 Philip Michael Sher Device for predicting and managing blood glucose by analyzing the effect of, and controlling, pharmacodynamic insulin equivalents
WO2008057384A2 (fr) 2006-11-02 2008-05-15 The University Of North Carolina At Chapel Hill Procédés et systèmes pour déterminer une vitesse de perfusion d'insuline intraveineuse afin de corriger l'hyperglycémie, de maintenir l'euglycémie et de prévenir l'hypoglycémie d'un patient
US20080306353A1 (en) 2006-11-03 2008-12-11 Douglas Joel S Calculation device for metabolic control of critically ill and/or diabetic patients
US20080119705A1 (en) 2006-11-17 2008-05-22 Medtronic Minimed, Inc. Systems and Methods for Diabetes Management Using Consumer Electronic Devices
WO2008067245A2 (fr) 2006-11-29 2008-06-05 University Of Maryland, Baltimore Détermination du débit de la pompe à insuline pour contrôle glycémique pointu
US20080139910A1 (en) 2006-12-06 2008-06-12 Metronic Minimed, Inc. Analyte sensor and method of using the same
US20080214919A1 (en) 2006-12-26 2008-09-04 Lifescan, Inc. System and method for implementation of glycemic control protocols
US7946985B2 (en) 2006-12-29 2011-05-24 Medtronic Minimed, Inc. Method and system for providing sensor redundancy
US7734323B2 (en) 2007-01-24 2010-06-08 Smiths Medical Asd, Inc. Correction factor testing using frequent blood glucose input
US20080220403A1 (en) * 2007-02-16 2008-09-11 Ohio University System and method for managing diabetes
US20080228056A1 (en) 2007-03-13 2008-09-18 Michael Blomquist Basal rate testing using frequent blood glucose input
US20080234943A1 (en) 2007-03-20 2008-09-25 Pinaki Ray Computer program for diabetes management
WO2008124478A1 (fr) 2007-04-04 2008-10-16 Pronia Medical Systems, Llc Systèmes, procédés et programme informatique destinés à une meilleure gestion de procédures médicales pour des patients suivant des protocoles médicaux
US20080255707A1 (en) 2007-04-10 2008-10-16 Hebblewhite Harry R Method and system for categorizing blood glucose tests at test time in a portable device or later in a downloading program and then analyzing the categories separately
CN101715317A (zh) 2007-04-19 2010-05-26 C.G.M.3有限公司 监控血液分析物水平的设备系统和方法
US8221345B2 (en) * 2007-05-30 2012-07-17 Smiths Medical Asd, Inc. Insulin pump based expert system
ES2693432T3 (es) 2007-06-21 2018-12-11 F. Hoffmann-La Roche Ag Método y dispositivo para prevenir la hipoglucemia
DK2186035T3 (en) 2007-06-29 2019-04-01 Hoffmann La Roche APPARATUS AND METHOD OF REMOTE CONTROL OF AN AMBULANT MEDICAL DEVICE
US20090036753A1 (en) * 2007-07-31 2009-02-05 King Allen B Continuous glucose monitoring-directed adjustments in basal insulin rate and insulin bolus dosing formulas
EP2178546A1 (fr) 2007-08-21 2010-04-28 ATP Marketing & Promotion AG Formulations à base de plantes pour contrôler le taux de glucose sanguin de patients atteints de diabète
US7943163B2 (en) 2007-08-22 2011-05-17 Response Scientific, Inc. Medical food or nutritional supplement, method of manufacturing same, and method of managing diabetes
US7998069B2 (en) 2007-09-11 2011-08-16 Roche Diagnostics Operations, Inc. Mask algorithms for health management systems
WO2009049245A1 (fr) 2007-10-11 2009-04-16 Optiscan Biomedical Corporation Synchronisation et configuration de dispositifs de surveillance des patients
TWI348910B (en) 2007-10-29 2011-09-21 Univ Taipei Medical Composition for controlling blood glucose and method thereof
US20100262434A1 (en) * 2007-12-13 2010-10-14 Shaya Steven A Method and apparatus to calculate diabetic sensitivity factors affecting blood glucose
US9199031B2 (en) 2007-12-26 2015-12-01 Ofer Yodfat Maintaining glycemic control during exercise
US20090177147A1 (en) 2008-01-07 2009-07-09 Michael Blomquist Insulin pump with insulin therapy coaching
US20090238879A1 (en) 2008-01-24 2009-09-24 Northwestern University Delivery scaffolds and related methods of use
US20100145725A1 (en) 2008-02-12 2010-06-10 Alferness Clifton A System and method for managing type 1 diabetes mellitus through a personal predictive management tool
US20100138453A1 (en) * 2008-02-12 2010-06-03 Alferness Clifton A System and method for generating a personalized diabetes management tool for diabetes mellitus
US20100145173A1 (en) * 2008-02-12 2010-06-10 Alferness Clifton A System and method for creating a personalized tool predicting a time course of blood glucose affect in diabetes mellitus
US20090214511A1 (en) 2008-02-21 2009-08-27 Trung Hong Tran Digestible compositions of inulin for managing blood glucose levels
US8317699B2 (en) 2008-02-29 2012-11-27 Roche Diagnostics Operations, Inc. Device and method for assessing blood glucose control
KR100946712B1 (ko) 2008-03-04 2010-03-12 주식회사 유비케어 인슐린투여량 조절 방법 및 시스템
US20090240127A1 (en) 2008-03-20 2009-09-24 Lifescan, Inc. Methods of determining pre or post meal time slots or intervals in diabetes management
US20090242399A1 (en) 2008-03-25 2009-10-01 Dexcom, Inc. Analyte sensor
US8396528B2 (en) 2008-03-25 2013-03-12 Dexcom, Inc. Analyte sensor
US20090247982A1 (en) 2008-03-27 2009-10-01 Lifescan Inc. Medical Device Mechanical Pump
US8185412B1 (en) 2008-03-28 2012-05-22 Mahesh Harpale Method and apparatus for chronic care treatment control with custom named-type factors and user estimation error correction
WO2009124111A2 (fr) 2008-04-01 2009-10-08 Trustees Of Boston University Capteur de glucose utilisant un dispositif nanoélectrique à semi-conducteurs
US9220456B2 (en) 2008-04-04 2015-12-29 Hygieia, Inc. Systems, methods and devices for achieving glycemic balance
US8600682B2 (en) 2008-04-04 2013-12-03 Hygieia, Inc. Apparatus for optimizing a patient's insulin dosage regimen
TWI394580B (zh) 2008-04-28 2013-05-01 Halozyme Inc 超快起作用胰島素組成物
US20100256047A1 (en) 2009-04-03 2010-10-07 Lifescan, Inc. Analyte Measurement and Management Device and Associated Methods
US20090281519A1 (en) 2008-05-12 2009-11-12 Rao R Harsha Automated system and method for diabetes control
US8924159B2 (en) 2008-05-30 2014-12-30 Abbott Diabetes Care Inc. Method and apparatus for providing glycemic control
JP5551691B2 (ja) 2008-06-26 2014-07-16 ラボラトリオス シラネス、エセ．ア．デ セ．べ． 血糖制御のための新規グリシン酸メトホルミン塩
IT1396465B1 (it) 2008-07-10 2012-12-14 B G Informatica S R L Metodo per la definizione e la gestione interattiva di un trattamento per il controllo del tasso glicemico nel paziente diabetico e dispositivo integrato che attua tale metodo
CN102548467A (zh) 2008-07-18 2012-07-04 生命扫描有限公司 分析物测量和管理装置及相关方法
KR100918776B1 (ko) 2009-04-20 2009-09-24 계명대학교 산학협력단 폴리에틸렌 글리콜과 갈레이트 카테킨을 이용한 혈당상승 억제 조성물
TWI532497B (zh) 2008-08-11 2016-05-11 曼凱公司 超快起作用胰島素之用途
EP2352456A1 (fr) 2008-08-20 2011-08-10 Bayer HealthCare LLC Système et procédés d'organisation de la fourniture d'une gestion de la santé
US20110177175A1 (en) 2008-09-23 2011-07-21 Rubicon Research Private Limited Dietary fiber compositions
EP2179661A1 (fr) 2008-10-23 2010-04-28 Generale Biscuit Biscuit comprenant de la gomme de guar sous forme de bâtonnet
WO2010056718A2 (fr) 2008-11-11 2010-05-20 Hygieia, Inc. Appareil et système de gestion du diabète
US8527208B2 (en) 2008-11-17 2013-09-03 Roche Diagnostics International Ag Prandial blood glucose excursion optimization method via computation of time-varying optimal insulin profiles and system thereof
US20120011125A1 (en) * 2008-12-23 2012-01-12 Roche Diagnostics Operations, Inc. Management method and system for implementation, execution, data collection, and data analysis of a structured collection procedure which runs on a collection device
US20100160740A1 (en) 2008-12-24 2010-06-24 Gary Cohen Use of Patterns in a Therapy Management System
US20100161346A1 (en) 2008-12-24 2010-06-24 Kristen Getschmann Systems and Methods for Providing Bolus Dosage Recommendations
US9330237B2 (en) 2008-12-24 2016-05-03 Medtronic Minimed, Inc. Pattern recognition and filtering in a therapy management system
US20100174553A1 (en) 2008-12-24 2010-07-08 Medtronic Minimed, Inc. Diabetes Therapy Management System
US8198320B2 (en) 2008-12-31 2012-06-12 Taipei Medical University Hypoglycemic activity of osthole
EA024878B1 (ru) 2009-01-07 2016-10-31 Хайдун Хуан Ген, кодирующий мутантную глюкокиназу человека, отличающуюся увеличенной стабильностью, и его применение для контроля глюкозы в крови или предупреждения и лечения нарушений углеводного обмена
WO2010083570A1 (fr) 2009-01-23 2010-07-29 The University Of Sydney Nouvelle thérapie pour maladie métabolique
US20140363794A1 (en) 2009-01-26 2014-12-11 EosHealth, Inc. Diabetic Patients Compliance and Outcomes by Publicizing that Increased Compliance Improves Blood Glucose Control and Outcomes
US10478100B2 (en) 2009-02-04 2019-11-19 Sanofi-Aventis Deutschland Gmbh Medical system and method for providing information for glycemic control
WO2010091129A1 (fr) 2009-02-04 2010-08-12 Abbott Diabetes Care Inc. Dispositif de test d'analyte multifonction et ses procédés
JP5783912B2 (ja) 2009-02-04 2015-09-24 サノフィ−アベンティス・ドイチュラント・ゲゼルシャフト・ミット・ベシュレンクテル・ハフツング 血糖応答情報に基づく血糖コントロールを提供するための医療システム及び方法
CA2750049C (fr) 2009-02-04 2021-02-23 Sanofi-Aventis Deutschland Gmbh Dispositif medical et procede fournissant des informations utilisables a des fins de regulation de la glycemie
AU2010210155B2 (en) 2009-02-04 2015-04-09 Sanofi-Aventis Deutschland Gmbh Medical device and method for glycemic control
KR20120047841A (ko) 2009-02-26 2012-05-14 몰 리서치 어플리케이션스 엘티디 당뇨병 관련 치료의 자동 감시를 위한 방법 및 시스템
EP2418972A1 (fr) 2009-04-14 2012-02-22 Abbott Laboratories Émulsions nutritionnelles à haute teneur en fibres pour le glucose du sang
US8467972B2 (en) 2009-04-28 2013-06-18 Abbott Diabetes Care Inc. Closed loop blood glucose control algorithm analysis
EP2433235A2 (fr) 2009-05-22 2012-03-28 Abbott Diabetes Care, Inc. Caractéristiques de sécurité pour système de délivrance d'insuline intégré
WO2010135670A2 (fr) 2009-05-22 2010-11-25 Abbott Diabetes Care Inc. Couche sécurité pour système de délivrance d'insuline intégré
WO2010135646A1 (fr) 2009-05-22 2010-11-25 Abbott Diabetes Care Inc. Caractéristiques ergonomiques pour système de délivrance d'insuline intégré
GB2471066A (en) 2009-06-10 2010-12-22 Dna Electronics Ltd A glucagon pump controller
US9687194B2 (en) 2009-06-17 2017-06-27 Medtronic Minimed, Inc. Closed-loop glucose and/or insulin control system
US9218453B2 (en) 2009-06-29 2015-12-22 Roche Diabetes Care, Inc. Blood glucose management and interface systems and methods
US20100331652A1 (en) 2009-06-29 2010-12-30 Roche Diagnostics Operations, Inc. Modular diabetes management systems
JP5654587B2 (ja) 2009-06-30 2015-01-14 ライフスキャン・インコーポレイテッドＬｉｆｅｓｃａｎ，Ｉｎｃ． 基礎インスリン療法を算出する分析物試験方法及び装置
WO2011002791A2 (fr) 2009-06-30 2011-01-06 Lifescan Scotland Limited Systèmes de gestion de diabète et procédés
CN102018766B (zh) 2009-09-16 2013-03-06 博仲盛景医药技术（北京）有限公司 一种植物提取物、其提取方法和用途及含有这种提取物的组合物
US20110071464A1 (en) 2009-09-23 2011-03-24 Medtronic Minimed, Inc. Semi-closed loop insulin delivery
KR101632308B1 (ko) 2009-09-23 2016-06-21 삼성전자주식회사 혈당 관리 정보를 제공하는 방법 및 장치
JP5657678B2 (ja) 2009-09-29 2015-01-21 ライフスキャン・スコットランド・リミテッドＬｉｆｅＳｃａｎ Ｓｃｏｔｌａｎｄ， Ｌｔｄ． 糖尿病管理のための分析物試験方法及びデバイス
US20110098548A1 (en) 2009-10-22 2011-04-28 Abbott Diabetes Care Inc. Methods for modeling insulin therapy requirements
EA201200745A1 (ru) 2009-11-15 2012-12-28 Дайверга Энерджи Корпорейшн Низкодифференциальные термороторные двигатели
US20110115894A1 (en) 2009-11-19 2011-05-19 Daniel Rogers Burnett Device and method for diagnosis and monitoring of neural, opthalmologic and retinal disorders
US8771251B2 (en) 2009-12-17 2014-07-08 Hospira, Inc. Systems and methods for managing and delivering patient therapy through electronic drug delivery systems
EP2512483A4 (fr) 2009-12-18 2013-06-05 Reddys Lab Ltd Dr Compositions de balaglitazone et méthodes associées
US8562589B2 (en) 2009-12-24 2013-10-22 Rani Therapeutics, Llc Swallowable drug delivery device and method of delivery
WO2011089600A1 (fr) 2010-01-20 2011-07-28 Medingo Ltd. Procédé et dispositif destinés à améliorer le contrôle de la glycémie
WO2011094352A1 (fr) 2010-01-27 2011-08-04 Baylor Research Institute Transfert de gène non viral in vivo de facteur de croissance endothélial vasculaire humain après transplantation d'îlots
US8579879B2 (en) 2010-02-19 2013-11-12 Medtronic Minimed, Inc. Closed-loop glucose control startup
US20110213332A1 (en) 2010-03-01 2011-09-01 Koorosh Mozayeny Medication delivery system
US9089292B2 (en) 2010-03-26 2015-07-28 Medtronic Minimed, Inc. Calibration of glucose monitoring sensor and/or insulin delivery system
WO2012161670A2 (fr) 2010-04-07 2012-11-29 Incube Labs, Llc Procédé pour traiter le diabètes ainsi que d'autres troubles de la régulation de la glycémie au moyen de cellules souches
US20120197358A1 (en) 2010-04-09 2012-08-02 Prescott Marvin A Light Therapy for Treating or Managing Diabetes and Metabolic Syndrome
JP2011237408A (ja) 2010-04-14 2011-11-24 Arkray Inc 生体情報測定装置、生体情報測定装置における照明方法、および生体情報測定方法
US9387279B2 (en) 2010-05-05 2016-07-12 The Governing Council Of The University Of Toronto Implantable-glucose responsive insulin delivery device
US8532933B2 (en) 2010-06-18 2013-09-10 Roche Diagnostics Operations, Inc. Insulin optimization systems and testing methods with adjusted exit criterion accounting for system noise associated with biomarkers
US10561785B2 (en) 2010-06-22 2020-02-18 Medtronic Minimed, Inc. Method and/or system for closed-loop control of glucose to a treatment range
US8543354B2 (en) 2010-06-23 2013-09-24 Medtronic Minimed, Inc. Glucose sensor signal stability analysis
US20120003339A1 (en) 2010-07-02 2012-01-05 Minacapelli Pompeo Compositions and methods for controlling blood glucose levels
US20120006100A1 (en) 2010-07-06 2012-01-12 Medtronic Minimed, Inc. Method and/or system for determining blood glucose reference sample times
WO2012004773A1 (fr) 2010-07-09 2012-01-12 Universite De Geneve Nouvelles utilisations d'inhibiteurs de nogo-a et procédés associés
US8870807B2 (en) 2010-07-19 2014-10-28 Surag Mantri Gastro-intestinal compression device
US8679170B2 (en) 2010-07-23 2014-03-25 Avacen, Inc. Methods and apparatus for therapeutic application of thermal energy
CN103003819A (zh) 2010-07-23 2013-03-27 霍夫曼-拉罗奇有限公司 考虑到身体活动对葡萄糖调节系统的影响的系统和方法
EP2603133A1 (fr) 2010-08-10 2013-06-19 Roche Diagnostics GmbH Procédé et système pour améliorer le contrôle de la glycémie
CA2807949C (fr) * 2010-08-13 2022-10-25 Intellimedicine, Inc. Systeme et procede pour la production de produits pharmaceutiques personnalises
EP2448432A1 (fr) 2010-08-17 2012-05-09 Abbott Laboratories Composition nutritionnelle comprenant du bêta-glucane de céréale et un extrait de salacia
US8206340B2 (en) 2010-08-18 2012-06-26 Thuban, Inc. Integrated glucose monitor and insulin injection pen with automatic emergency notification
US20120058942A1 (en) 2010-09-03 2012-03-08 John Dupre Methods for controlling blood-glucose levels in insulin-requiring subjects
WO2012047800A1 (fr) 2010-10-06 2012-04-12 Abbott Laboratories Procédés pour augmenter l'absorption de glucose par le muscle et réduire le temps de récupération du muscle employant un mélange d'acides aminés
US8588687B2 (en) 2010-10-15 2013-11-19 Roche Diagnostics Operations, Inc. Coexistence of multiple radios in a medical device
TW201305098A (zh) 2010-10-27 2013-02-01 Prometic Biosciences Inc 用於糖尿病的化合物和藥物組合物
US8657746B2 (en) 2010-10-28 2014-02-25 Medtronic Minimed, Inc. Glucose sensor signal purity analysis
US8919180B2 (en) 2010-10-28 2014-12-30 Medtronic Minimed, Inc. Determination and application of glucose sensor reliability indicator and/or metric
EP2633456B1 (fr) 2010-10-31 2019-09-04 Trustees of Boston University Système de régulation de la glycémie
US8349612B2 (en) 2010-11-15 2013-01-08 Roche Diagnostics Operations, Inc. Guided structured testing kit
CN103415287A (zh) 2010-11-16 2013-11-27 新泽西医科和牙科大学 以安全的线粒体解偶联化学剂治疗ii型糖尿病和糖尿病相关疾病
WO2012068552A1 (fr) 2010-11-19 2012-05-24 Edison Pharmaceuticals, Inc. Méthodes d'amélioration du contrôle de la glycémie
CN102462736B (zh) 2010-11-19 2015-07-15 北京唐安营养保健品有限公司 肉桂水提取物的制备方法
US8329232B2 (en) 2010-11-23 2012-12-11 Beijing Tang-An Nutrition & Healthcare Products Co. Ltd. Process for preparing water extract of cinnamon
ES2396844B1 (es) 2010-12-01 2014-01-27 Universitat Politècnica De Catalunya Sistema y método para la estimación simultánea y no invasiva de la glucosa en la sangre, nivel de glucocorticoides y presión arterial
WO2012073987A1 (fr) 2010-12-01 2012-06-07 キッコーマン株式会社 Transformant de e. coli, procédé de production de glucose déshydrogénase liée à la flavine à l'aide de celui-ci, et glucose déshydrogénase mutante liée à la flavine
US9469844B2 (en) 2010-12-02 2016-10-18 Kikkoman Corporation Flavin-bound glucose dehydrogenases, a method for producing a flavin-bound glucose dehydrogenase, and yeast transformant used for the same
US20130316029A1 (en) 2010-12-21 2013-11-28 Nestec S.A. Methods and compositions suitable for managing blood glucose in animals
US20120173151A1 (en) 2010-12-29 2012-07-05 Roche Diagnostics Operations, Inc. Methods of assessing diabetes treatment protocols based on protocol complexity levels and patient proficiency levels
WO2012097064A1 (fr) 2011-01-13 2012-07-19 Abbott Laboratories Compositions nutritionnelles et procédés pour contrôler le glucose dans le sang
KR20130128451A (ko) 2011-01-31 2013-11-26 카딜라 핼쓰캐어 리미티드 지방이상증의 치료
WO2012122520A1 (fr) 2011-03-10 2012-09-13 Abbott Diabetes Care Inc. Dispositif de surveillance de substance à analyser à fonctions multiples et procédés d'utilisation
DK2685895T3 (en) 2011-03-17 2019-01-21 Univ Newcastle System for self-monitoring and regulation of blood glucose
MX339374B (es) 2011-04-29 2016-05-13 Inst De Investigación En Química Aplic S A De C V Cocristales ionicos con base en metformina.
US8690934B2 (en) 2011-05-09 2014-04-08 The Invention Science Fund I, Llc Method, device and system for modulating an activity of brown adipose tissue in a vertebrate subject
US8766803B2 (en) 2011-05-13 2014-07-01 Roche Diagnostics Operations, Inc. Dynamic data collection
US8755938B2 (en) 2011-05-13 2014-06-17 Roche Diagnostics Operations, Inc. Systems and methods for handling unacceptable values in structured collection protocols
ES2774491T3 (es) 2011-06-07 2020-07-21 Wisconsin Alumni Res Found Terapia génica con insulina basada en hepatocitos para la diabetes
AU2012271361B2 (en) 2011-06-17 2017-03-02 Halozyme, Inc. Continuous subcutaneous insulin infusion methods with a hyaluronan degrading enzyme
US10610154B2 (en) 2011-06-23 2020-04-07 University Of Virginia Patent Foundation Unified platform for monitoring and control of blood glucose levels in diabetic patients
WO2013016573A1 (fr) 2011-07-26 2013-01-31 Glysens Incorporated Capteur à logement hermétique implantable dans tissu
US8667293B2 (en) 2011-08-11 2014-03-04 Roche Diagnostics Operations, Inc. Cryptographic data distribution and revocation for handheld medical devices
GB2493712B (en) 2011-08-12 2014-07-02 Gene Onyx Ltd Insulin pump
CN103917974A (zh) 2011-08-18 2014-07-09 诺沃—诺迪斯克有限公司 具有用于处置数据的器件的药物递送装置
WO2013040712A1 (fr) 2011-09-20 2013-03-28 Afexa Life Sciences Inc. Composition comprenant ku ding cha et du ginseng pour le contrôle des taux de glycémie
WO2013049381A1 (fr) 2011-09-28 2013-04-04 Abbott Diabetes Care Inc. Procédés pour la gestion de surveillance d'analyte et la gestion de données de mesure d'analyte, et articles de fabrication associés à ceux-ci
US20130085682A1 (en) 2011-10-04 2013-04-04 Roche Diagnostics Operations, Inc. Memory card usage with blood glucose devices
EP2763722B1 (fr) 2011-10-07 2016-08-10 Novo Nordisk A/S Système pour déterminer la position d'un élément basé sur des détecteurs magnétiques triaxiaux
US20130144283A1 (en) 2011-12-02 2013-06-06 Medtronic Ardian Luxembourg S.A.R.L. Treatment of polycystic ovary syndrome using renal neuromodulation and associated systems and methods
CA2858566A1 (fr) 2011-12-09 2013-06-13 Joel R. Ehrenkranz Systeme et procedes permettant de surveiller une consommation d'aliments
TW201333870A (zh) 2011-12-21 2013-08-16 艾登工具股份有限公司 決定病人胰島素療法的系統及方法
WO2013101749A1 (fr) 2011-12-29 2013-07-04 Latitude Pharmaceuticals, Inc. Nanoémulsions de glucagon stabilisées
US20130172688A1 (en) 2011-12-29 2013-07-04 Roche Diagnostics Operations, Inc. Diabetes management application for mobile phone
WO2013108262A1 (fr) 2012-01-18 2013-07-25 Zota Health Care Ltd Combinaison synergique pour le traitement de la neuropathie diabétique
US10694983B2 (en) 2012-01-19 2020-06-30 Medtronic Minimed, Inc. Method and/or system for assessing a patient's glycemic response
EP2805275A1 (fr) 2012-01-20 2014-11-26 Mueller-Wolf, Martin Système, procédé et produit programme d'ordinateur d'un « appareil indima » pour des soins de santé individualisés et en collaboration
KR101166907B1 (ko) 2012-02-08 2012-07-19 최병철 혈당조절 및 생리활성 작용을 하는 서목태(쥐눈이콩) 엑기스와 그 제조방법
EP2812706A1 (fr) 2012-02-10 2014-12-17 Alere Switzerland GmbH Essai et procédé pour déterminer la résistance à l'insuline
ES2421956B1 (es) 2012-03-02 2014-09-29 Moehs Ibérica S.L. Nueva forma cristalina de sulfato de sitagliptina
US9080939B2 (en) 2012-03-06 2015-07-14 National Central University Method of measuring glycosylated protein proportion by AC impedance method
US10342592B2 (en) 2012-03-07 2019-07-09 Medtronic Ardian Luxembourg S.A.R.L. Selective modulation of renal nerves
WO2013134548A2 (fr) 2012-03-08 2013-09-12 Medtronic Ardian Luxembourg S.A.R.L. Neuromodulation ovarienne, systèmes et procédés associés
US20140337041A1 (en) 2012-03-30 2014-11-13 Joseph Madden Mobile Application for Defining, Sharing and Rewarding Compliance with a Blood Glucose Level Monitoring Regimen
US20130281796A1 (en) 2012-04-20 2013-10-24 Broadmaster Biotech Corp. Biosensor with exercise amount measuring function and remote medical system thereof
BR112013012240B1 (pt) 2012-05-16 2019-10-29 Melaleuca Inc composição de suplemento dietético em pó
US20150174209A1 (en) 2012-05-25 2015-06-25 Amylin Pharmaceuticals. Llc Insulin-pramlintide compositions and methods for making and using them
US20130317316A1 (en) 2012-05-25 2013-11-28 City Of Hope Carbohydrate modeling methods, systems, and devices
US9150482B2 (en) 2012-06-06 2015-10-06 Development Center For Biotechnology GLP-1 potentiators from hedychium coronarium and their applications
US20130338629A1 (en) * 2012-06-07 2013-12-19 Medtronic Minimed, Inc. Diabetes therapy management system for recommending basal pattern adjustments
US9536449B2 (en) 2013-05-23 2017-01-03 Medibotics Llc Smart watch and food utensil for monitoring food consumption
US9254099B2 (en) 2013-05-23 2016-02-09 Medibotics Llc Smart watch and food-imaging member for monitoring food consumption
US9861744B2 (en) 2012-06-25 2018-01-09 International Business Machines Corporation Managing blood glucose levels
US20140012117A1 (en) 2012-07-09 2014-01-09 Dexcom, Inc. Systems and methods for leveraging smartphone features in continuous glucose monitoring
US9990581B2 (en) * 2012-07-11 2018-06-05 Roche Diabetes Care, Inc. Insulin dosage assessment and recommendation system
US8492426B1 (en) 2012-07-12 2013-07-23 Anis Ahmad Use of carvedilol for treatment of diabetes mellitus
US20140024907A1 (en) 2012-07-19 2014-01-23 Lifescan, Inc. Method and system to indicate hyperglycemia or hypoglycemia for people with diabetes
WO2014024201A1 (fr) 2012-08-06 2014-02-13 Ramchandran T S Régulation et contrôle des glycémies d'un sujet et promotion du métabolisme cellulaire par application d'ondes ultrasonores aériennes basse fréquence (20 khz à 70 khz) et machine pour produire les ondes ultrasonores aériennes pour celles-ci
AR092077A1 (es) 2012-08-10 2015-03-18 Sanofi Aventis Deutschland Sistema medico
WO2014028607A1 (fr) 2012-08-14 2014-02-20 Abbott Laboratories Compositions nutritives à faible indice glycémique pour conserver la masse musculaire et améliorer la composition corporelle chez les diabétiques
US9504411B2 (en) 2012-08-28 2016-11-29 Roche Diabetes Care, Inc. Diabetes manager for glucose testing and continuous glucose monitoring
US9662445B2 (en) 2012-08-30 2017-05-30 Medtronic Minimed, Inc. Regulating entry into a closed-loop operating mode of an insulin infusion system
US10496797B2 (en) * 2012-08-30 2019-12-03 Medtronic Minimed, Inc. Blood glucose validation for a closed-loop operating mode of an insulin infusion system
US9171343B1 (en) * 2012-09-11 2015-10-27 Aseko, Inc. Means and method for improved glycemic control for diabetic patients
US9278200B2 (en) 2012-09-19 2016-03-08 Mackay Memorial Hospital Flow control device, system and method for controlling the flow of body fluid
EP2727587A1 (fr) 2012-10-30 2014-05-07 Pharnext Compositions, traitements et utilisations pour le traitement du diabète et troubles connexes par le contrôle du taux de glycémie
JP6460998B2 (ja) 2012-11-13 2019-01-30 インビクタス バイオテクノロジー プロプライエタリー リミテッド トコトリエノールの経粘膜送達
CN104780931B (zh) 2012-12-17 2018-05-18 星系有限公司 Bt脂肽作为用于肥胖和相关疾病的治疗剂的用途
US9351670B2 (en) 2012-12-31 2016-05-31 Abbott Diabetes Care Inc. Glycemic risk determination based on variability of glucose levels
US9192509B2 (en) 2013-03-11 2015-11-24 Avacen, Inc. Methods and apparatus for therapeutic application of thermal energy including blood viscosity adjustment
US20140296943A1 (en) 2013-03-14 2014-10-02 Biological Illumination, Llc Method for controlling blood glucose levels and digestion cycles
JP6417561B2 (ja) 2013-03-15 2018-11-07 アボット ダイアベティス ケア インコーポレイテッドＡｂｂｏｔｔ Ｄｉａｂｅｔｅｓ Ｃａｒｅ Ｉｎｃ． 検体モニタリング装置と関連づけられた装置、システム、および方法、並びにそれらを組み込んだ装置
ES2799305T3 (es) 2013-03-15 2020-12-16 Becton Dickinson Co Adaptador inteligente para dispositivos de infusión
US9795737B2 (en) 2013-03-15 2017-10-24 Animas Corporation Method and system for closed-loop control of an artificial pancreas
WO2014149781A1 (fr) 2013-03-15 2014-09-25 Cercacor Laboratories, Inc. Système de monitorage physiologique basé sur le nuage
WO2014162549A1 (fr) 2013-04-04 2014-10-09 テルモ株式会社 Système de gestion de la glycémie
US20140303466A1 (en) 2013-04-06 2014-10-09 Applivate, LLC Computer-Based Monitoring of Blood Glucose Levels
US9237866B2 (en) 2013-04-29 2016-01-19 Birch Narrows Development, LLC Blood glucose management
US20160089500A1 (en) 2013-05-21 2016-03-31 Novo Nordisk A/S Drug Delivery Device with Means for Disabling Activation of Expelling Mechanism
CA2913285A1 (fr) 2013-05-23 2014-11-27 Iphenotype Llc Procedes et systemes d'assistance a des personnes, des fournisseurs de produits et/ou des fournisseurs de services
US9529385B2 (en) 2013-05-23 2016-12-27 Medibotics Llc Smart watch and human-to-computer interface for monitoring food consumption
US9056049B2 (en) 2013-05-30 2015-06-16 Chin Yuan Huang Micro-particle comprising a protein extract from sweet potato for extending satiety and controlling blood glucose and lipid levels
CN103432572B (zh) 2013-06-21 2018-09-18 四川大学华西医院 一种恒河猴糖尿病肾病模型的制备方法
US9482635B2 (en) 2013-06-25 2016-11-01 Animas Corporation Glucose-measurement systems and methods presenting icons
US20160038077A1 (en) 2013-06-27 2016-02-11 Inspark Technologies, Inc. Systems, Devices, and/or Methods for Identifying Time Periods of Insufficient Blood Glucose Testing
US9486580B2 (en) * 2014-01-31 2016-11-08 Aseko, Inc. Insulin management

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6024699A (en) * 1998-03-13 2000-02-15 Healthware Corporation Systems, methods and computer program products for monitoring, diagnosing and treating medical conditions of remotely located patients
US20070179434A1 (en) * 2005-12-08 2007-08-02 Stefan Weinert System and method for determining drug administration information
US20110251472A1 (en) * 2006-06-16 2011-10-13 Stefan Weinert System for collecting patient information for diabetes management
US20090006061A1 (en) * 2007-06-27 2009-01-01 Roche Diagnostics Operations, Inc. System for developing patient specific therapies based on dynamic modeling of patient physiology and method thereof
US20150217054A1 (en) * 2014-01-31 2015-08-06 Aseko, Inc. Insulin Management

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP3337402A4 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10046113B2 (en) 2011-12-21 2018-08-14 Monarch Medical Technologies, Llc Systems and methods for determining insulin therapy for a patient
US10842935B2 (en) 2011-12-21 2020-11-24 Monarch Medical Technologies, Llc Systems and methods for determining insulin therapy for a patient
EP3642789A4 (fr) * 2017-06-20 2021-01-27 Chan, Sidney Soong-Ling Procédé et système de surveillance d'un plan de traitement du diabète à
US11793471B2 (en) 2017-06-20 2023-10-24 Sidney Soong-Ling CHAN Method and system for monitoring a diabetes treatment plan

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US20190326005A1 (en) 2019-10-24
IL256999B (en) 2021-06-30
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US10380328B2 (en) 2019-08-13
US11200988B2 (en) 2021-12-14
JP6858751B2 (ja) 2021-04-14
CA2993275A1 (fr) 2017-02-23
EP3337402A4 (fr) 2019-04-24
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US20170053101A1 (en) 2017-02-23
AU2016308953B2 (en) 2020-09-10
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